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Objective To investigate the value of gray-scale median (GSM) of carotid plaque in predicting the progression from transient ischemic attack (TIA) to cerebral infarction. Methods The clinical data of 156 patients with carotid plaque who were admitted to our hospital from February 2020 to March 2022 was collected. The patients were divided into TIA group and non-TIA group according to their history of TIA. The two groups were compared for differences in the volume, thickness, and GSM of carotid plaque. The patients with different clinical features in the TIA group were compared for differences in the volume, thickness, GSM, and ABCD2 score of carotid plaque. The incidence of cerebral infarction within one year in the patients with TIA was recorded. The receiver operating characteristic (ROC) curve was used to analyze the value of GSM in predicting cerebral infarction. Results Compared with the non-TIA group, the TIA group had a significantly reduced GSM value (t=2.638,P

Background/Aim. The best treatment for the occlusion of the largest artery in the thigh is a femorodistal (FD) bypass. Ankle-brachial index (ABI) and multidetector computed tomographic (MDCT) angiography are the gold standards for diagnosing peripheral arterial occlusive disease. The finite element analysis (FEA) method can help measure the quantity of blood flow and arterial pressure in the arteries in the leg. The aim of this study was to examine the possibility of using the FEA method in predicting the outcome of FD bypass surgery. Methods. The study involved 45 patients indicated for FD arterial reconstruction from December 1, 2021, to March 31, 2023. Each patient underwent pre- and postoperative MDCT angiography of the arteries of the lower extremities, on the basis of which, with the use of FEA, models were made for measuring ABI. All patients had their ABI measured pre-operatively and postoperatively using the Doppler ultrasound and sphygmomanometer. Based on the findings of the preoperative MDCT angiography, postoperative virtual surgical models were created using the FEA method, on which ABI were also measured. The values of ABI were divided into five groups: ABI measured preoperatively (ABI pre-op), ABI measured postoperatively (ABI post-op), ABI measured on FEA models based on the MDCT findings [ABI (sim) pre-op], ABI sim post-op, and ABI measured on virtual surgery model [ABI sim post-op (virtual)]. The ABI of the models were statistically compared with preoperative and postoperative measurements done on patients. Results. The values based on the virtual ABI model did not show significant differences compared to the values obtained on patients and values obtained with the FEA method using MDCT angiography (p < 0.001). A strong statistically significant correlation was shown between the virtual ABI and the values obtained by the other two methods, measured on the postoperative MDCT angiography model and virtual postoperative model (p < 0.001). Conclusion. Virtual simulation based on the MDCT angiography parameters of peripheral blood vessels can be successfully used to predict the immediate outcome of the FD bypass surgery.

Objectives: An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical referral suggestion for IMLLs, and to validate it in the setting of nontraumatic emergency neuroradiology. Methods: A DL system was developed in 747 patients with IMLLs ranging 30 diseases who underwent pre- and post-contrast T1-weighted (T1CE), FLAIR, and diffusion-weighted imaging (DWI). A DL system that segments IMLLs, classifies tumourous conditions, and suggests clinical referral among surgery, systematic work-up, medical treatment, and conservative treatment, was developed. The system was validated in an independent cohort of 130 emergency patients, and performance in referral suggestion and tumour discrimination was compared with that of radiologists using receiver operating characteristics curve, precision-recall curve analysis, and confusion matrices. Multiparametric interpretable visualisation of high-relevance regions from layer-wise relevance propagation overlaid on contrast-enhanced T1WI and DWI was analysed. Results: The DL system provided correct referral suggestions in 94 of 130 patients (72.3%) and performed comparably to radiologists (accuracy 72.6%, McNemar test; p =.942). For distinguishing tumours from non-tumourous conditions, the DL system (AUC, 0.90 and AUPRC, 0.94) performed similarly to human readers (AUC, 0.81~0.92, and AUPRC, 0.88~0.95). Solid portions of tumours showed a high overlap of relevance, but non-tumours did not (Dice coefficient 0.77 vs. 0.33, p <.001), demonstrating the DL's decision. Conclusions: Our DL system could appropriately triage patients using multiparametric MRI and provide interpretability through multiparametric heatmaps, and may thereby aid neuroradiologic diagnoses in emergency settings. Clinical relevance statement: Our AI triages patients with raw MRI images to clinical referral pathways in brain intra-axial mass-like lesions. We demonstrate that the decision is based on the relative relevance between contrast-enhanced T1-weighted and diffusion-weighted images, providing explainability across multiparametric MRI data. Key Points: • A deep learning (DL) system using multiparametric MRI suggested clinical referral to patients with intra-axial mass-like lesions (IMLLs) similar to radiologists (accuracy 72.3% vs. 72.6%). • In the differentiation of tumourous and non-tumourous conditions, the DL system (AUC, 0.90) performed similar with radiologists (AUC, 0.81–0.92). • The DL's decision basis for differentiating tumours from non-tumours can be quantified using multiparametric heatmaps obtained via the layer-wise relevance propagation method. [ABSTRACT FROM AUTHOR]

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This study proposes a new diagnostic tool for automatically extracting discriminative features and detecting temporomandibular joint disc displacement (TMJDD) accurately with artificial intelligence. We analyzed the structural magnetic resonance imaging (MRI) images of 52 patients with TMJDD and 32 healthy controls. The data were split into training and test sets, and only the training sets were used for model construction. U-net was trained with 100 sagittal MRI images of the TMJ to detect the joint cavity between the temporal bone and the mandibular condyle, which was used as the region of interest, and classify the images into binary categories using four convolutional neural networks: InceptionResNetV2, InceptionV3, DenseNet169, and VGG16. The best models were InceptionV3 and DenseNet169; the results of InceptionV3 for recall, precision, accuracy, and F1 score were 1, 0.81, 0.85, and 0.9, respectively, and the corresponding results of DenseNet169 were 0.92, 0.86, 0.85, and 0.89, respectively. Automated detection of TMJDD from sagittal MRI images is a promising technique that involves using deep learning neural networks. It can be used to support clinicians in diagnosing patients as having TMJDD. [ABSTRACT FROM AUTHOR]

Our study aims to assess the robustness of myocardial radiomic texture features (RTF) to segmentation variability and variations across scanners with different field strengths, addressing concerns about reliability in clinical practices. We conducted a retrospective analysis on 45 pairs of CMR T1 maps from 15 healthy volunteers using 1.5 T and 3 T Siemens scanners. Manual left ventricular myocardium segmentation and a deep learning-based model with Monte Carlo Dropout generated masks with different levels of variability and 1023 RTFs extracted from each region of interest (ROI). Reproducibility: the extent to which RTFs extracted from 1.5 T and 3 T images are consistent, and repeatability: the extent to which RTFs extracted from multiple segmentation runs at the same field strength agree with each other, were measured by the intraclass correlation coefficient (ICC). We categorized ICC values as excellent, good, moderate, and poor. We reported the proportion of RTFs that fell in each category. The proportion of RTFs with excellent repeatability decreased as the proportion of ROI pixels in congruence across segmentation runs decreased. Up to 31% of RTFs showed excellent repeatability, while 35% showed good repeatability across segmentation runs from the manually generated masks. Across scanners (i.e., 1.5 T vs 3 T), only 7% exhibited good reproducibility. While our results demonstrate RTF sensitivity to differences in field strength and segmentation variability, we identified certain preprocessing filters and feature classes that are less sensitive to these variations and, as such, may be good candidates for imaging biomarkers or building machine-learning models., Competing Interests: Declarations. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval: This study was designated as non-human subjects research by the institutional IRB, given no identifying information was saved during the collection of the normative scans., (© 2025. The Author(s).)

A four-dimensional phase-contrast magnetic resonance imaging sequence with respiratory-controlled adaptive k-space reordering (ReCAR-4DPC) offers potential benefits of improved scan efficiency and motion robustness. The purpose of this study was to evaluate the reproducibility of flow measurement using this technique and to compare hemodynamic metrics obtained to two-dimensional phase contrast MRI (2DPC)-derived metrics of the thoracic aorta. ReCAR-4DPC was performed with identical scan parameters in 15 healthy volunteers (6M,9F, mean [range] 37 [23-47] years) and 11 patients with thoracic aortic dissection (6M,5F, 56 [31-81] years) and acquisition time was recorded. Peak systolic velocity (PSV), average flow (AF) and net forward volume (NFV) were quantified by two readers for ReCAR-4DPC at ascending, descending and diaphragmatic aorta levels. Reference standard 2DPC measurements at the same levels were performed by a separate experienced cardiovascular radiologist. ReCAR-4DPC intra-reader agreement, inter-reader agreement, inter-scan repeatability and concordance with 2DPC-derived metrics (all segments combined) were evaluated with Lin's concordance correlation coefficient (LCCC) and reduced major axis regression. The overall average ± SD MRI acquisition time of all subjects was 11:59 ± 3:57 min, with shorter average times (9:37 ± 1:57 min) in healthy volunteers compared to patients (15:13 ± 3:44 min). There was near-perfect intra-reader, inter-reader and inter-scan concordance (LCCC for all metrics > 0.97, > 0.98 and > 0.92 respectively) for ReCAR-4DPC. Concordance with 2DPC was also high (LCCC all > 0.89), with overall minimally lower PSV, AF and NFV values derived from ReCAR-4DPC compared to reference 2DPC derived metrics. ReCAR-4DPC is a reproducible and relatively fast approach for comprehensive measurement of thoracic aortic flow metrics, with robust correlation to conventional 2DPC., Competing Interests: Declarations. Conflict of interests: DS and NJ are employees and shareholders of Siemens Healthcare. Non-employee authors had control of study design and data collection. Ethical approval: This study was a prospective study with institutional ethics approval. Informed consent: Written informed consent was obtained from all the subjects. Consent for publication: The authors affirm that human research participants provided informed consent for publication of the images in Figs. 2, 3 and 4., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Prior studies have demonstrated an association between retinal vascular features and cardiovascular disease (CVD), however most studies have only evaluated a few simple parameters at a time. Our aim was to determine whether a deep-learning artificial intelligence (AI) model could be used to predict CVD outcomes from routinely obtained diabetic retinal screening photographs and to compare its performance to a traditional clinical CVD risk score., Methods: We included 6127 individuals with type 2 diabetes without myocardial infarction or stroke prior to study entry. The cohort was divided into training (70%), validation (10%) and testing (20%) cohorts. Clinical 10-year CVD risk was calculated using the pooled cohort equation (PCE) risk score. A polygenic risk score (PRS) for coronary heart disease was also obtained. Retinal images were analysed using an EfficientNet-B2 network to predict 10-year CVD risk. The primary outcome was time to first major adverse CV event (MACE) including CV death, myocardial infarction or stroke., Results: 1241 individuals were included in the test cohort (mean PCE 10-year CVD risk 35%). There was a strong correlation between retinal predicted CVD risk and the PCE risk score (r = 0.66) but not the polygenic risk score (r = 0.05). There were 288 MACE events. Higher retina-predicted risk was significantly associated with increased 10-year risk of MACE (HR 1.05 per 1% increase; 95% CI 1.04-1.06, p < 0.001) and remained so after adjustment for the PCE and polygenic risk score (HR 1.03; 95% CI 1.02-1.04, p < 0.001). The retinal risk score had similar performance to the PCE (both AUC 0.697) and when combined with the PCE and polygenic risk score had significantly improved performance compared to the PCE alone (AUC 0.728). An increase in retinal-predicted risk within 3 years was associated with subsequent increased MACE likelihood., Conclusions: A deep-learning AI model could accurately predict MACE from routine retinal screening photographs with a comparable performance to traditional clinical risk assessment in a diabetic cohort. Combining the AI-derived retinal risk prediction with a coronary heart disease polygenic risk score improved risk prediction. AI retinal assessment might allow a one-stop CVD risk assessment at routine retinal screening., Competing Interests: Declarations. Ethics approval and consent to participate: Data analysis was conducted within the Health Informatics Centre Trusted Research Environment under its overarching ethics approval for approved researchers to conduct research within their secure environment (East of Scotland Research Ethics Committee reference: 18/ES/0126). As the study data are de-identified, consent from individual patients was not required. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests., (© 2025. The Author(s).)

Objective: We assessed the predictive efficacy of automatically quantified retinal vascular tortuosity from the fundus pictures of patients with sickle cell disease (SCD) without evident retinopathy., Methods: Retinal images were obtained from 31 healthy and 31 SCD participants using fundus imaging and analyzed using a novel computational automated metric assessment. The local and global vessel tortuosity and their relationship with systemic disease parameters were analyzed based on the images., Results: SCD arteries had an increased local tortuosity index compared to the controls (0.0007 ± 0.0019 vs. 0.0006 ± 0.0014, p = 0.019). Furthermore, the SCD patients had wider vessel caliber mainly in the arteries (14.68 ± 5.3 vs. 14.06 ± 5.3, p < 0.001). The SCD global tortuosity did not differ significantly from that of the controls (p = 0.598). The female participants had significantly reduced retinal vessel tortuosity indices compared to the male participants (p = 0.018)., Conclusion: Retinal arterial tortuosity and caliber were reliable and objective measures that could be used as a non-invasive prognostic and diagnostic indicator in sickle cell retinopathy. Further studies are required to correlate these local vascular parameters to systemic risk factors and monitor their progression and change over time., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The expansion of tricuspid valve (TV) interventions has underscored the need for accurate and reproducible three-dimensional (3D) transthoracic echocardiographic (TTE) tools for evaluating the tricuspid annulus and for 3D normal values of this structure. The aims of this study were to develop new semi-automated software for 3D TTE analysis of the tricuspid annulus, compare its accuracy and reproducibility against those of multiplanar reconstruction (MPR) reference, and determine normative values., Methods: Three-dimensional TTE images of 113 patients with variable degrees of tricuspid regurgitation were analyzed using the new semiautomated software and conventional MPR methodology (as the reference standard), each by three independent readers. For each measured parameter, intertechnique agreement was assessed using linear regression and Bland-Altman analyses, and interreader variability using intraclass correlation coefficients and coefficients of variation. Additionally, 3D TTE data sets of 789 subjects from the WASE (World Alliance Societies of Echocardiography) study were analyzed using this new software to determine normal values for each tricuspid annular (TA) parameter., Results: Semiautomated measurements showed excellent agreement with MPR reference values for all TA measurements: high correlations (all r values >0.8) and minimal biases. All measurements were more reproducible than with MPR: higher intraclass correlation coefficients (0.94-0.96 vs 0.82-0.90) and lower coefficients of variation (5%-12% vs 8%-18%). Sex- and age-related differences were identified in 3D normal values of most TA parameters. Dynamic analysis showed that TA parameters vary throughout the cardiac cycle, reaching minimal values at end-systole and maximum values in late diastole., Conclusions: Novel software for semiautomated analysis of TA geometry and dynamics provides accurate and reproducible measurements. Normal values of TA dimensions, parsed by sex and age, are reported., Competing Interests: Conflicts of Interest Ms. Blitz, Ms. Sauber, Mr. Calvarons, Mr. Degel, Dr. Rucki, Mr. Blankenhagen, and Dr. Schreckenberg are employees of Philips. These authors had no role in study design, conduct, analysis, and interpretation of the findings. Dr. Lang receives grants as a speaker from Philips. Drs. Cotella, Clement, and Tomaselli have received research grants from Philips for unrelated projects., (Copyright © 2024 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.)

Bladder cancer (BC) remains a major disease burden in terms of incidence, morbidity, mortality, and economic cost. Deciphering the intrinsic molecular subtypes and identification of key drivers of BC has yielded successful novel therapeutic strategies. Advances in computational and digital pathology are reshaping the field of anatomical pathology. This review offers an update on the most relevant computational algorithms in digital pathology that have been proposed to enhance BC management. These tools promise to enhance diagnostics, staging, and grading accuracy and streamline efficiency while advancing practice consistency. Computational applications that enable intrinsic molecular classification, predict response to neoadjuvant therapy, and identify targets of therapy are also reviewed., (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)

3-dimensional (3D) intracardiac echocardiography (ICE) is emerging as a promising complement and potential alternative to transesophageal echocardiography for imaging guidance in structural heart interventions. To establish standardized practices, our multidisciplinary expert position statement serves as a comprehensive guide for the appropriate indications and utilization of 3D-ICE in various structural heart procedures. The paper covers essential aspects such as the fundamentals of 3D-ICE imaging, basic views, and workflow recommendations specifically tailored for ICE-guided structural heart procedures, such as transeptal puncture, device closure of intracardiac structures, and transcatheter mitral and tricuspid valve interventions. Current challenges, future directions, and training requirements to ensure operator proficiency are also discussed, thereby promoting the safety and efficacy of this innovative imaging modality to support expanding its future clinical applications., Competing Interests: Funding Support and Author Disclosures Dr Tang has received speaker honoraria and served as a physician proctor, consultant, advisory board member, transcatheter aortic valve replacement publications committee member, APOLLO (Transcatheter Mitral Valve Replacement With the Medtronic Intrepid™ TMVR System in Patients With Severe Symptomatic Mitral Regurgitation) trial screening committee member, and IMPACT MR steering committee member for Medtronic; has received speaker honoraria and served as a physician proctor, consultant, advisory board member, and the TRILUMINATE (Trial to Evaluate Treatment With Abbott Transcatheter Clip Repair System in Patients With Moderate or Greater Tricuspid Regurgitation) anatomical eligibility and publications committee member for Abbott Structural Heart; has served as an advisory board member for Boston Scientific and Jena-Valve; has served as a consultant for NeoChord, Shockwave Medical, Peija Medical, and Shenqi Medical Technology; and has received speaker honoraria from Siemens Healthineers. Dr Hahn has received speaker fees from Abbott Vascular, Edwards Lifesciences, and Philips Healthcare; and has institutional consulting contracts (for which she has received no direct compensation) with Abbott Vascular, Boston Scientific, Edwards Lifesciences, Medtronic, and Novartis. Dr Alkhouli has served as a consultant to Boston Scientific and Abbott. Dr Aman has served on the advisory board for Abbott Structural Heart, as a consultant for Abbott Structural Heart and Philips, and on the speaker bureau for Abbott Structural Heart and Philips. Dr Berti has been a proctor for Abbott, St. Jude, and Edwards. Dr Chadderdon has received consulting fees from Medtronic and Edwards Lifesciences; and has received grant support from Medtronic and GE Healthcare. Dr Fam has been a consultant to Edwards Lifesciences, Abbott, and Cardiovalve. Dr Ho has served as a consultant for NeoChord. Dr Kliger has been a consultant and has received speaker honoraria from Edwards Lifesciences, Medtronic, and Siemens. Dr Kodali has received consultant fees from Admedus and Dura Biotech; holds equity in Dura Biotech, Microinterventional Devices, Thubrikar Aortic Valve, Supira, Admedus, TriFlo, and Anona; and has received institutional grant support from Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, and JenaValve. Dr Latib has served on advisory boards or as a consultant for Medtronic, Boston Scientific, Edwards Lifesciences, Abbott, and VDyne. Dr Mahadevan has been a consultant for Edwards Lifesciences. Dr Nair has served as a consultant for and receives honoraria for speaking engagements from Boston Scientific, Johnson and Johnson, and Medtronic. Dr Packer has received research funding from Abbott, Biosense Webster, Boston Scientific/EPT, CardioInsight, EBAmed, Medtronic, NeuCures, Siemens, St. Jude Medical, Thermedical, National Institutes of Health, Robertson Foundation, Vital Project Funds, Xenter, and the Mr and Mrs J. Michael Cook Fund. Dr Praz has received travel expenses from Abbott Vascular, Edwards Lifesciences, and Polares Medical. Dr Rogers has been a consultant for Abbott Structural Heart and Boston Scientific. Dr Ruf has received speaker, consulting, and proctoring fees from Abbott Laboratories and Edwards Lifesciences. Dr Singh has been a consultant for Abbott Structural Heart and Philips. Dr van Mieghem has received research grant support from Abbott Vascular, Boston Scientific, Medtronic, Edwards Lifesciences, Biotronik, Daiichi Sankyo, Abiomed, and PulseCath BV. Dr Vannan has received research grants and speaker honoraria from Piedmont Heart Institute for Abbott, Medtronic, Edwards Lifesciences, Philips, Siemens Healthineers, and GE Healthcare. Dr Yadav has been a consultant and speaker for Edwards Lifesciences, Abbott, Dasi Simulations, and Shockwave Medical. Dr Zahr has received institutional grant support from Edwards Lifesciences and Medtronic. Dr von Bardeleben has been a consultant, advisory board member, TRILUMINATE trial eligibility committee member, and speaker for Abbott Vascular and Edwards Lifesciences. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2025 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnostic/therapeutic pathways and ultimately improve patient outcomes., Objectives: The authors aimed to develop and validate a fully automated machine learning (ML)-based echocardiography workflow for grading MR severity., Methods: ML algorithms were trained on echocardiograms from 2 observational cohorts and validated in patients from 2 additional independent studies. Multiparametric echocardiography core laboratory MR assessment served as ground truth. The machine was trained to measure 16 MR-related parameters. Multiple ML models were developed to find the optimal parameters and preferred ML model for MR severity grading., Results: The preferred ML model used 9 parameters. Image analysis was feasible in 99.3% of cases and took 80 ± 5 seconds per case. The accuracy for grading MR severity (none to severe) was 0.80, and for significant (moderate or severe) vs nonsignificant MR was 0.97 with a sensitivity of 0.96 and specificity of 0.98. The model performed similarly in cases of eccentric and central MR. Patients graded as having severe MR had higher 1-year mortality (adjusted HR: 5.20 [95% CI: 1.24-21.9]; P = 0.025 compared with mild)., Conclusions: An automated multiparametric ML model for grading MR severity is feasible, fast, highly accurate, and predicts 1-year mortality. Its implementation in clinical practice could improve patient care by facilitating referral to specialized clinics and access to evidence-based therapies while improving quality and efficiency in the echocardiography laboratory., Competing Interests: Funding Support and Author Disclosures Mr Jiang is an employee of Us2.ai. Mr Hummel is an employee of Us2.ai. Mr Frost is an employee of Us2.ai. Dr Lam is supported by a Clinician Scientist Award from the National Medical Research Council of Singapore; has received research support from NovoNordisk and Roche Diagnostics; has served as consultant or on the Advisory Board/Steering Committee/Executive Committee for Alleviant Medical, Allysta Pharma, Amgen, AnaCardio AB, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, CardioRenal, Cytokinetics, Darma Inc, EchoNous Inc, Eli Lilly, Impulse Dynamics, Intellia Therapeutics, Ionis Pharmaceutical, Janssen Research and Development LLC, Medscape/WebMD Global LLC, Merck, Novartis, Novo Nordisk, Prosciento Inc, Quidel Corporation, Radcliffe Group Ltd, Recardio Inc, ReCor Medical, Roche Diagnostics, Sanofi, Siemens Healthcare Diagnostics and Us2.ai; has served as cofounder and non-executive director of Us2.ai; has a patent pending (PCT/SG2016/050217; Method for diagnosis and prognosis of chronic heart failure); and holds U.S. Patent No. 10,702,247 for automated clinical workflow that recognizes and analyses 2-dimensional and Doppler echo images for cardiac measurements and the diagnosis, prediction, and prognosis of heart disease. Dr Shah is supported by grants from the U.S. National Institutes of Health (National Heart, Lung, and Blood Institute; U54 HL160273, R01 HL140731, and R01 HL149423), AstraZeneca, Corvia Medical, and Pfizer; and has received consulting fees from Abbott, AstraZeneca, Alleviant, Amgen, Aria CV, Axon Therapies, Bayer, Boehringer Ingelheim, Boston Scientific, Bristol-Myers Squibb, Cyclerion, Cytokinetics, Edwards Lifesciences, Eidos, Imara, Impulse Dynamics, Intellia, Ionis, Lilly, Merck, Metabolic Flux, MyoKardia, NGM Biopharmaceuticals, Novartis, Novo Nordisk, Pfizer, Prothena, Regeneron, Rivus, Sardocor, Shifamed, Tenax, Tenaya, Ultromics, and United Therapeutics. Dr Lund is supported by Karolinska Institutet, the Swedish Research Council (grant 523-2014-2336), the Swedish Heart Lung Foundation (grants 20150557, 20190310), and the Stockholm County Council (grants 20170112, 20190525); and unrelated to the present work, Dr Lund has received grants, consulting, and honoraria from Abbot, Alleviant, AstraZeneca, Bayer, Biopeutics, Boehringer Ingelheim, Edwards, Merck/Merck Sharp & Dohme, Novartis, Novo Nordisk, Owkin, Pharmacosmos, Vifor Pharma; and has stock ownership in AnaCardio. Dr Stone has received Speaker or other honoraria from Cook, Terumo, QOOL Therapeutics, and Orchestra Biomed; is a consultant to Valfix, TherOx, Vascular Dynamics, Robocath, HeartFlow, Gore, Ablative Solutions, Miracor, Neovasc, V-Wave, Abiomed, Ancora, MAIA Pharmaceuticals, Vectorious, Reva, Matrizyme, Cardiomech; and has equity/options from Ancora, Qool Therapeutics, Cagent, Applied Therapeutics, Biostar family of funds, SpectraWave, Orchestra Biomed, Aria, Cardiac Success, MedFocus family of funds, and Valfix. Dr Swaminathan has received consulting fees from US2.ai. Dr Weissman is the Associate Director of an academic core laboratory with research institutional grants/agreements (MedStar Health) with Us2.ai, Ultromics, TOMTEC, GE, Caption Health, egnite, Abbott, Edwards, Medtronic, Boston Scientific, Corcym, Ancora Heart, Neovasc, InnovHeart, and Polares Medical. Dr Asch is the director of an academic core laboratory with research institutional grants/agreements (MedStar Health) with Us2.ai, Ultromics, TOMTEC, GE, Caption Health, egnite, Abbott, Edwards, Medtronic, Boston Scientific, Corcym, Ancora Heart, Neovasc, InnovHeart, Polares Medical, and Foldax; and is on the Scientific Advisory Board (unpaid) for Us2.ai, Ultromics, and Abbott. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2025 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background and Objective: The injury of the cholinergic white matter pathway underlies cognition decline in patients with silent cerebrovascular disease (SCD) with white matter hyperintensities (WMH) of vascular origin. However, the evaluation of the cholinergic white matter pathway is complex with poor consistency. We established an intelligent algorithm to evaluate WMH in the cholinergic pathway., Methods: Patients with SCD with WMH of vascular origin were enrolled. The Cholinergic Pathways Hyperintensities Scale (CHIPS) was used to measure cholinergic white matter pathway impairment. The intelligent algorithm used a deep learning model based on convolutional neural networks to achieve WMH segmentation and CHIPS scoring. The diagnostic value of the intelligent algorithm for moderate-to-severe cholinergic pathway injury was calculated. The correlation between the WMH in the cholinergic pathway and cognitive function was analysed., Results: A number of 464 patients with SCD were enrolled in internal training and test set. The algorithm was validated using data from an external cohort comprising 100 patients with SCD. The sensitivity, specificity and area under the curve of the intelligent algorithm to assess moderate and severe cholinergic white matter pathway injury were 91.7%, 87.3%, 0.903 (95% CI 0.861 to 0.952) and 86.5%, 81.3%, 0.868 (95% CI 0.819 to 0.921) for the internal test set and external validation set. for the. The general cognitive function, execution function and attention showed significant differences among the three groups of different CHIPS score (all p<0.05)., Discussion: We have established the first intelligent algorithm to evaluate the cholinergic white matter pathway with good accuracy compared with the gold standard. It helps more easily assess the cognitive function in patients with SCD., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Objective: This study aims to investigate whether a deep learning approach incorporating retinal blood vessels can effectively identify ischemic stroke patients with a high burden of White Matter Hyperintensities (WMH) using Nuclear Magnetic Resonance Imaging (MRI) as the gold standard., Methods: In this cross-sectional study, we evaluated 263 ischemic stroke inpatients who had acquired both retinal fundus images and MRI images. The primary outcome was the diagnostic WMH on MRI brain, defined as different degrees of the age-related white matter changes (ARWMC) grade (<2 or ≥2). We developed a deep-learning network model with retinal fundus images to estimate WMH., Results: The mean age of the patient cohort was 60.8 years, with 196 individuals (74.5%) being male. The prevalence of risk factors was as follows: hypertension in 237 (90.1%), diabetes in 109 (41.4%), hyperlipidemias in 84 (31.9%), coronary heart disease in 37 (14.1%), hyperhomocysteinemia in 70 (26.6%), and hyperuricemia in 73 (27.8%). Severe WMH defined as global ARWMC grade ≥2 was found in 139 (52.9%) participants. Using binocular fundus images, we achieved an F1 score of 0.811 and a Macro Accuracy of 0.811 in the ARWMC classification task. Additionally, we conducted experiments by progressively occluding fundus images to assess the relationship between different areas of the fundus images and ARWMC prediction., Interpretation: Our study presents a novel deep learning model designed to detect a high burden of WMH using binocular fundus images in ischemic stroke patients. We have conducted initial investigations into the predictive significance of various fundus image areas for WMH identification. These findings underscore the need for broader data collection, further model training, and prospective data validation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objectives: The aim of this study is to develop and validate a model for predicting axial spondyloarthritis (axSpA) based on sacroiliac joint (SIJ)-MRI imaging findings and clinical risk factors., Methods: The study is implemented on the data of 942 patients which contains of 707 patients with axSpA and 235 patients with non-axSpA. To begin with, the patients were split into training (n = 753) and validation (n = 189) cohorts. Secondly, multiple assessors manually extract the features of active inflammation (bone marrow edema) and structural lesions (erosions, sclerosis, ankylosis, joint space changes, and fat lesions). Meanwhile, we utilize 11 machine learning models and TabNet to develop imaging models, which contain six clinical risk factors for clinical models and combined clinical-imaging models. Finally, the diagnostic performance of the aforementioned models was evaluated in the validation cohort including accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, F1-score, and Matthew's correlation coefficient (MCC)., Results: Six features were extracted from the imaging findings. The combined clinical-imaging models outperform the clinical and imaging models. In contrast, the combined clinical-imaging model via TabNet (CCMRT) achieved the optimal AUC of 0.93(95% CI: 0.89, 0.97). Furthermore, it is observed that the bilateral joint space changes and right-sided erosions, HLA-B27 positivity, and CRP values significantly affected axSpA diagnostic prediction., Conclusion: The prediction model based on clinical risk factors and SIJ-MRI imaging features can distinguish axSpA and non-axSpA effectively. In addition, the TabNet demonstrates superior diagnostic efficacy compared with machine learning models., (© 2024 Asia Pacific League of Associations for Rheumatology and John Wiley & Sons Australia, Ltd.)

Myocarditis, characterized by inflammation of the myocardial tissue, presents substantial risks to cardiovascular functionality, potentially precipitating critical outcomes including heart failure and arrhythmias. This investigation primarily aims to identify the optimal cardiovascular magnetic resonance imaging (CMRI) views for distinguishing between normal and myocarditis cases, using deep learning (DL) methodologies. Analyzing CMRI data from a cohort of 269 individuals, with 231 confirmed myocarditis cases and 38 as control participants, we implemented an innovative DL framework to facilitate the automated detection of myocarditis. Our approach was divided into single-frame and multi-frame analyses to evaluate different views and types of acquisitions for optimal diagnostic accuracy. The results demonstrated a weighted accuracy of 96.9%, with the highest accuracy achieved using the late gadolinium enhancement (LGE) 2-chamber view, underscoring the potential of DL in distinguishing myocarditis from normal cases on CMRI data., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent to publish: All authors have read and agreed to the published version of the manuscript., (© 2024. The Author(s).)

Purpose: To compare the application of two contrast-enhanced time-resolved magnetic resonance angiography sequences on an aortic disease patient cohort: the conventional Cartesian-sampling-based, Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) sequence, and the radial-sampling-based Golden-angle RAdial Sparse Parallel (GRASP) sequence. TWIST is highly sensitive to patient movement, which can lead to blurring and reduced sharpness of vascular structures, particularly in dynamic regions like the aorta. Such motion artifacts can compromise diagnostic accuracy. Radial-sampling-based techniques are less sensitive to motion than cartesian sampling and are expected to improve the image quality in body parts subjected to motion., Methods: 30 patients (60.9 ± 16.1y.o.) with various aortic diseases underwent a 1.5T magnetic resonance angiography examination. Assessment of image quality in the ascending aorta (AA), descending aorta (DA), and abdominal aorta (AbA) on a 4-point Likert scale (1 = excellent, 4 = non-diagnostic) as well as max. aortic diameters (Dmax) were performed. T-test and multilevel mixed-effect proportional-odds models were used for the image analysis., Results: GRASP offered superior depiction of vascular structures in terms of vascular contrast for qualitative analysis (TWIST, reader 1: 1.6 ± 0.5; reader 2: 1.9 ± 0.4; reader 3: 1.1 ± 0.4; GRASP, reader 1: 1.5 ± 0.5; reader 2: 1.4 ± 0.5; reader 3: 1.0 ± 0.2) and vessel sharpness for qualitative (TWIST, reader 1: 1.9 ± 0.6; reader 2: 1.6 ± 0.6; reader 3: 2.0 ± 0.3; GRASP, reader 1: 1.4 ± 0.6; reader 2: 1.2 ± 0.4; reader 3: 1.3 ± 0.6) and quantitative analysis (TWIST, AA = 0.12 ± 0.04, DA = 0.12 ± 0.03, AbA = 0.11 ± 0.03; GRASP, AA = 0.20 ± 0.05, DA = 0.22 ± 0.06, AbA = 0.20 ± 0.05). Streaking artefacts of GRASP were more visible compared to TWIST (TWIST, reader 1: 2.2 ± 0.6; reader 2: 1.9 ± 0.3; reader 3: 2.0 ± 0.5; GRASP, reader 1: 2.6 ± 0.6; reader 2: 2.3 ± 0.5; reader 3: 2.8 ± 0.6). Aortic Dmax comparison among the sequence showed no clinical relevance., Conclusion: GRASP outperformed TWIST in SNR, vessel sharpness, and reduction in image blurring; streaking artefacts were stronger with GRASP, but did not affect diagnostic image quality., Competing Interests: Declarations. Ethical approval: Ethical adherence: The study was approved by the local institution review board and by the local IRB (Reference number 2022 − 1936). No studies involving animals were performed. Written informed consent was obtained from all subjects according to our institutional guidelines. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

(1) Background: Unruptured Intracranial Aneurysms (UIAs) are common blood vessel malformations, occurring in up to 3 % of healthy adults. Magnetic Resonance Angiography (MRA) is frequently used for the screening of UIAs due to its high resolution in vascular anatomy. However, T2-Weighted Magnetic Resonance Imaging (T2WI) is a standard sequence utilized for the majority of outpatient head scans. By employing a sequence such as T2WI, there is a possible shift towards early detection of UIAs through opportunistic screening. Here, we assess a Deep Learning Algorithm (DLA) developed to assist radiologists in identifying and reporting UIAs on T2WI in a routine clinical setting. (2) Methods: A DLA was trained on a set of 110 patients undergoing an MR head scan with the gold standard set by two radiology experts. Eight radiologists were given a cohort of 50 cranial T2WI studies and asked for a routine report. After a 10-day washout period, they reviewed the same cases randomized and supported by the DLA predictions. We assessed changes in sensitivity, specificity, accuracy, and false positives. (3) Results: During routine reporting, the models' assistance improved the sensitivity of the eight participants by an average of 36.19 and the accuracy by 10.00 percentage points. (4) Conclusion: Our results indicate the potential benefit of deep learning to improve radiologists' detection of UIAs during routine reporting. From this, we can infer that the combination of T2WI with our DLA supports opportunistic screening, suggesting potential approaches for future research and application., Competing Interests: Declaration of competing interest BT is a Medical Advisor at Floy GmbH. LG and AMK are Clinical Scientists at Floy GmbH. EJGA is the Head of Clinical Research at Floy GmbH. AG is Machine Learning Engineer at Floy GmbH. Correspondence and requests for materials should be addressed to BT., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: The aim of this study was to evaluate the effects of the kV Assist technique on the radiation dose and image quality of coronary computed tomography angiography (CCTA). Methods: In this retrospective case-control study, 179 patients with the mean age of 58.45±11.86 years, who had undergone CCTA test using the kv assist technique, were considered as the study group; and 141 patients with the mean age of 58.24±11.37 years, who had previously undergone CCTA with the usual 120 kV protocol, were considered as the control group. The two groups were compared in terms of image quality criteria including noise, contrast-to-noise ratio, signal-to-noise ratio, CT numbers of the left coronary artery, and left ventricular chamber, and radiation dose criteria including effective dose and carcinogenic risk. Results: The effective dose (5.05 ± 2.16 vs. 6.63 ± 3.04 mSv; P < 0.001) and overall risk of carcinogenesis (3.89 ± 1.63 vs. 5.11 ± 2.71 in 10,000 people; P < 0.001) reduced by about 23%in the study group compared to the control group. No significant differences were observed between the two groups in terms of noise, signal-to-noise ratio, and left coronary artery CT number (P > 0.050). Left ventricular chamber CT number and contrast to noise ratio were higher in the study group than the control group (P < 0.001). Conclusion: Using the kV Assist technique can reduce the effective dose and the carcinogenic risk of CCTA without loss of image quality.

Artificial intelligence in diagnostic cardiac-imaging platforms is advancing rapidly. In particular, artificial intelligence algorithms have increased the efficiency and accuracy of echocardiographic cardiovascular imaging, resulting in more complex echocardiographic imaging techniques and expanded use among noncardiologists. Here, we provide an overview of real-world applications of artificial intelligence in echocardiography including automatic high-quality computer-optimized image acquisition sequences, automated measurements, and algorithms for the rapid and accurate interpretation of cardiac physiology. These advances will not replace physicians but will improve their productivity, workflow, and diagnostic performance. [ABSTRACT FROM AUTHOR]

Background: Integration of artificial intelligence enhances precision, yielding dependable evaluations of left ventricular volumes and ejection fraction despite image quality variations. Commercial software like HeartModel AI provides fully automated 3DE quantification, simplifying the measurement of left chamber volumes and ejection fraction. In this manuscript, we present a cross-sectional study to assess and compare the diagnostic accuracy of automated 3D echocardiography (HeartModel AI ) to the standard Cardiac Magnetic Resonance Imaging in patients with dilated cardiomyopathy., Methods: In this cross-sectional study, 30 patients with dilated cardiomyopathy referring to the Tehran Heart Center with cardiac magnetic resonance imaging and comprehensive 3D transthoracic echocardiography within 24 h were included. All 3D volume analysis was performed with fully automated quantification software (HeartModel AI ) using 3D images of 2,3, and 4-chamber views at the end of systole and diastole., Results: Excellent Inter- and Intra-observer correlation coefficient was reported for HeartModel AI software for all indexes. HeartModel AI displayed a remarkable correlation with cardiac magnetic resonance for left ventricular end-systolic volume index (r = 0.918 and r = 0.911); nevertheless, it underestimated left ventricular end-systolic volume index and left ventricular end-diastolic volume index. Conversely, ejection fraction, stroke volume, and left ventricular mass were overestimated. It was found that manual contour correction can enhance the accuracy of automated model estimations, particularly concerning EF in participants needing correction., Conclusion: HeartModel AI software emerges as a rapid and viable imaging approach for evaluating the left ventricle's structure and function. In our study, LV volumes assessed by HeartModel AI demonstrated strong correlations with cardiac magnetic resonance imaging., Competing Interests: Declarations. Ethics approval and consent to participate: All procedures were conducted by the 1964 Declaration of Helsinki and its later extensions. Written informed consent was obtained for participation and publication. The ethics committee of Tehran University of Medical Sciences (TUMS) reviewed and approved this study (IR.TUMS.THC.REC.1402.065). Consent for publication: Written informed consent was obtained for participation and publication. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiac disorders and is characterized by different phenotypes of left ventricular hypertrophy with and without obstruction. The effects of left ventricular outflow tract (LVOT) obstruction based on different anatomies may be hemodynamically relevant and influence therapeutic decision making. Cardiovascular magnetic resonance (CMR) provides anatomical information. We aimed to identify different shapes of LVOT-obstruction using Cardiovascular Magnetic Resonance (CMR). The study consisted of two parts: An in-vivo experiment for shape analysis and in-vitro part for the assessment of its hemodynamic consequences. In-vivo a 3D depiction of the LVOT was created using a 3D multi-slice reconstruction from 2D-slices (full coverage cine stack with 7 slices and a thickness of 5-6 mm with no gap) in 125 consecutive HOCM patients (age = 64.17 +/- 12.655; female n = 42). In-vitro an analysis of the LVOT regarding shape and flow behavior was conducted. For this purpose, 2D and 4D measurements were performed on 3D printed phantoms which were based on the anatomical characteristics of the in-vivo study, retrospectively. The in-vivo study identified three main shapes named K- (28.8%), X- (51.2%) and V-shape (10.4%) and a mixed one (9.6%). By analyzing the in-vitro flow measurements every shape showed an individual flow profile in relation to the maximum velocity in cm/s. Here, the V-shape showed the highest value of velocity (max. 138.87 cm/s). The X-shape was characterized by a similar profile but with lower velocity values (max. 125.39 cm/s), whereas the K-shape had an increase of the velocity without decrease (max. 137.11 cm/s). For the first time three different shapes of LVOT-obstruction could be identified. These variants seem to affect the hemodynamics in HOCM., Competing Interests: Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose., (© 2024. The Author(s).)

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets. In the test set, the Dice similarity coefficients (DSC) were 0.966 ± 0.025 and 0.982 ± 0.017 for the lumen and EEM, respectively. Even at sites of extensive attenuation, the frame-level performance was excellent (DSCs > 0.96 for the lumen and EEM). The model (vs. the expert) showed a better temporal consistency for contouring the EEM. The agreement between the model- vs. the expert-derived cross-sectional and volumetric measurements was excellent in the independent retrospective cohort (all, intra-class coefficients > 0.94). The model-derived percent atheroma volume > 52.5% (area under curve 0.70, sensitivity 71% and specificity 67%) and plaque burden at the minimal lumen area site (area under curve 0.72, sensitivity 72% and specificity 66%) best predicted 3-year cardiac death and nonculprit-related target vessel revascularization, respectively. In the stented segment, the DSCs > 0.96 for contouring lumen and EEM were achieved. Applied to the 60-MHz IVUS images, the DSCs were > 0.97. In the external cohort with 45-MHz IVUS, the DSCs were > 0.96. The deep learning model accurately delineated vascular geometry, which may be cost-saving and support clinical decision-making., Competing Interests: Declarations Informed consent The authors declare that this report does not contain any personal information that could lead to the identification of the patients. Commercial plans There has not been ongoing or planned commercialization efforts based on the developed model. Competing interests Kim H & Lee G is an employee of Mediwhale Inc., Seoul, Korea. Min D is an employee of and Ingradient Inc., Seoul, Korea. Other authors report no conflicts of interest regarding this manuscript., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and brain of living subjects. In medical imaging, edge detection is used to accurately identify and delineate important structures and boundaries inside the images. To improve edge sharpness, edge detection frequently requires the inclusion of low-level features. Herein, a machine learning approach is needed to automate the edge detection of multicellular aggregates of distinctly labeled blood cells within the microcirculation. In this work, the Structured Adaptive Boosting Trees algorithm (AdaBoost.S) is proposed as a contribution to overcome some of the edge detection challenges related to medical images. Algorithm design is based on the observation that edges over an image mask often exhibit special structures and are interdependent. Such structures can be predicted using the features extracted from a bigger image patch that covers the image edge mask. The proposed AdaBoost.S is applied to detect multicellular aggregates within blood vessels from the fluorescence lung intravital images of mice exposed to e-cigarette vapor. The predictive capabilities of this approach for detecting platelet-neutrophil aggregates within the lung blood vessels are evaluated against three conventional machine learning algorithms: Random Forest, XGBoost and Decision Tree. AdaBoost.S exhibits a mean recall, F-score, and precision of 0.81, 0.79, and 0.78, respectively. Compared to all three existing algorithms, AdaBoost.S has statistically better performance for recall and F-score. Although AdaBoost.S does not outperform Random Forest in precision, it remains superior to the XGBoost and Decision Tree algorithms. The proposed AdaBoost.S is widely applicable to analysis of other fluorescence intravital microscopy applications including cancer, infection, and cardiovascular disease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could be considered to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Aim: To assess in a phantom and in a clinical study the influence of different reconstruction parameters on quantitative SPECT/CT values in the assessment of cardiac transthyretin amyloidosis (ATTR-CA)., Method: A hybrid SPECT/CT camera with a proprietary software for SPECT/CT-based quantification of myocardial uptake of 99m Tc-DPD was used. Images were reconstructed with 6 different protocols, differing in iterations, subset and Gaussian filter. Quantitative values were tested in phantom and clinical studies across different reconstruction protocols. Values were automatically calculated both as kBq/ml and as maximum, mean and peak standardized uptake value (SUV)., Results: The standard parameters provided by the manufacturer (reconstruction 1) yielded higher accuracy in quantifying, with measuring 97.1% of the expected activity in the phantom. Reconstructions with higher Gaussian filter caused a systematic underestimation of quantified values of 27.2% (p < 0.01). Results were replicated in the clinical study consisting of 155 patients with suspected ATTR-CA, wherein changing the number of iterations and subsets was not associated with a statistically significant difference in quantitative values compared to reconstruction 1, while a higher Gaussian filter caused inaccurate quantification with up to 24% of difference measured., Conclusion: Different reconstruction parameters can impact quantitative values on 99m Tc-DPD SPECT/CT. Therefore, parameters should be maintained consistently across different acquisitions and different centres., Competing Interests: Declarations Competing interests The authors declare no competing interests. Disclosures Robin Schepers has received speaker honoraria from Siemens. Christoph Gräni receives funding from the Swiss National Science foundation, InnoSuisse, CAIM foundation, GAMBIT foundation and Novartis biomedical research foundation, outside of the submitted work. Axel Rominger has received research support and speaker honoraria from Siemens. Federico Caobelli is currently supported by a research grant by Siemens Healthineers and receives speakers Honoraria by Bracco AG and Pfizer AG for matters not related to the present manuscript. All other authors have no competing interests to disclose., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm 2 . We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Background: Cerebral small vessel disease (CSVD) includes vascular disorders characterized by heterogeneous pathomechanisms and different neuropathological clinical manifestations. Cognitive dysfunction in CSVD is associated with reductions in structural covariance networks (SCNs). A majority of research conducted on SCNs focused on group-level analysis. However, it is crucial to investigate the individualized variations in order to gain a better understanding of heterogeneous disorders such as CSVD. Therefore, this study aimed to utilize individualized differential structural covariance network (IDSCN) analysis to detect individualized structural covariance aberration., Methods: A total of 35 healthy controls and 33 CSVD patients with cognitive impairment participated in this investigation. Using the regional gray matter volume in their T1 images, the IDSCN was constructed for each participant. Finally, the differential structural covariance edges between the two groups were determined by comparing their IDSCN using paired-sample t-tests. On the basis of these differential edges, the two subtypes of cognitively impaired CSVD patients were identified., Results: The findings revealed that the differential structural covariance edges in CSVD patients with cognitive impairment showed a highly heterogeneous distribution, with the edges primarily cross-distributed between the occipital lobe (specifically inferior occipital gyrus and cuneus), temporal lobe (specifically superior temporal gyrus), and the cerebellum. To varying degrees, the inferior frontal gyrus and the superior parietal gyrus were also distributed. Subsequently, a correlation analysis was performed between the resulting differential edges and the cognitive scale scores. A significant negative association was observed between the cognitive scores and the differential edges distributed in the inferior frontal gyrus and inferior occipital gyrus, the superior temporal gyrus and inferior occipital gyrus, and within the temporal lobe. Particularly in the cognitive domain of attention, the two subtypes separated by differential edges exhibited differences in cognitive scale scores [Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)]. The differential edges of the subtype 1, characterized by lower cognitive level, were mainly cross-distributed in the limbic lobe (specifically the cingulate gyrus and hippocampus), the parietal lobe (including the superior parietal gyrus and precuneus), and the cerebellum. In contrast, the differential edges of the subtype 2 with a relatively high level of cognition were distributed between the cuneus and the cerebellum., Conclusions: The differential structural covariance was investigated between the healthy controls and the CSVD patients with cognitive impairment, showing that differential structural covariance existed between the two groups. The edge distributions in certain parts of the brain, such as cerebellum and occipital and temporal lobes, verified this. Significant associations were seen between cognitive scale scores and some of those differential edges .The two subtypes that differed in both differential edges and cognitive levels were also identified. The differential edges of subtype 1 with relatively lower cognitive levels were more distributed in the cingulate gyrus, hippocampus, superior parietal gyrus, and precuneus. This could potentially offer significant benefits in terms of accurate diagnosis and targeted treatment of heterogeneous disorders such as CSVD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

EGFR mutations are a major prognostic factor in lung adenocarcinoma. However, current detection methods require sufficient samples and are costly. Deep learning is promising for mutation prediction in histopathological image analysis but has limitations in that it does not sufficiently reflect tumor heterogeneity and lacks interpretability. In this study, we developed a deep learning model to predict the presence of EGFR mutations by analyzing histopathological patterns in whole slide images (WSIs). We also introduced the EGFR mutation prevalence (EMP) score, which quantifies EGFR prevalence in WSIs based on patch-level predictions, and evaluated its interpretability and utility. Our model estimates the probability of EGFR prevalence in each patch by partitioning the WSI based on multiple-instance learning and predicts the presence of EGFR mutations at the slide level. We utilized a patch-masking scheduler training strategy to enable the model to learn various histopathological patterns of EGFR. This study included 868 WSI samples from lung adenocarcinoma patients collected from three medical institutions: Hallym University Medical Center, Inha University Hospital, and Chungnam National University Hospital. For the test dataset, 197 WSIs were collected from Ajou University Medical Center to evaluate the presence of EGFR mutations. Our model demonstrated prediction performance with an area under the receiver operating characteristic curve of 0.7680 (0.7607-0.7720) and an area under the precision-recall curve of 0.8391 (0.8326-0.8430). The EMP score showed Spearman correlation coefficients of 0.4705 (p = 0.0087) for p.L858R and 0.5918 (p = 0.0037) for exon 19 deletions in 64 samples subjected to next-generation sequencing analysis. Additionally, high EMP scores were associated with papillary and acinar patterns (p = 0.0038 and p = 0.0255, respectively), whereas low EMP scores were associated with solid patterns (p = 0.0001). These results validate the reliability of our model and suggest that it can provide crucial information for rapid screening and treatment plans., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

In recent years, it has become clear that artificial intelligence (AI) models can achieve high accuracy in specific pathology-related tasks. An example is our deep-learning model, designed to automatically detect serous tubal intraepithelial carcinoma (STIC), the precursor lesion to high-grade serous ovarian carcinoma, found in the fallopian tube. However, the standalone performance of a model is insufficient to determine its value in the diagnostic setting. To evaluate the impact of the use of this model on pathologists' performance, we set up a fully crossed multireader, multicase study, in which 26 participants, from 11 countries, reviewed 100 digitalized H&E-stained slides of fallopian tubes (30 cases/70 controls) with and without AI assistance, with a washout period between the sessions. We evaluated the effect of the deep-learning model on accuracy, slide review time and (subjectively perceived) diagnostic certainty, using mixed-models analysis. With AI assistance, we found a significant increase in accuracy (p < 0.01) whereby the average sensitivity increased from 82% to 93%. Further, there was a significant 44 s (32%) reduction in slide review time (p < 0.01). The level of certainty that the participants felt versus their own assessment also significantly increased, by 0.24 on a 10-point scale (p < 0.01). In conclusion, we found that, in a diverse group of pathologists and pathology residents, AI support resulted in a significant improvement in the accuracy of STIC diagnosis and was coupled with a substantial reduction in slide review time. This model has the potential to provide meaningful support to pathologists in the diagnosis of STIC, ultimately streamlining and optimizing the overall diagnostic process., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Background: Accurate quantification of right ventricular (RV) volumes and function is crucial for the management of congenital heart diseases., Aims: We aimed to assess the feasibility and accuracy of bedside analysis using new RV quantification software from three-dimensional transthoracic echocardiography in children with or without congenital heart disease, and to compare measurements with cardiac magnetic resonance imaging., Methods: We included paediatric patients with congenital heart disease (106 patients) responsible for RV volume overload and a control group (30 patients). All patients underwent three-dimensional transthoracic echocardiography using a Vivid E95 ultrasound system. RV end-diastolic and end-systolic volumes and RV ejection fraction were obtained using RV quantification software. Measurements were compared between RV quantification and cardiac magnetic resonance imaging in 27 patients., Results: Bedside RV quantification analysis was feasible in 133 patients (97.8%). Manual contour adjustment was necessary in 126 patients (93%). The mean time of analysis was 62±42s. RV end-diastolic and end-systolic volumes were larger in the congenital heart disease group than the control group: median 85.0 (interquartile range 29.5) mL/m 2 vs 55.0 (interquartile range 20.5) mL/m 2 for RV end-diastolic volume and 42.5 (interquartile range 15.3) mL/m 2 vs 29.0 (interquartile range 11.8) mL/m 2 for RV end-systolic volume, respectively. Good agreement for RV end-diastolic and end-systolic volumes and RV ejection fraction was found between RV quantification and magnetic resonance imaging measurements. RV quantification software underestimated RV end-diastolic volume/body surface area by 3mL/m 2 and RV ejection fraction by 2.1%, and overestimated RV end-systolic volume/body surface area by 0.2mL/m 2 ., Conclusions: We found good feasibility and accuracy of bedside RV quantification analysis from three-dimensional transthoracic echocardiography in children with or without congenital heart disease. RV quantification could be a reliable and non-invasive method for RV assessment in daily practice, facilitating appropriate management and follow-up care., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Background: Late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMR) is a valuable cardiovascular imaging technique. Segmentation of cardiac chambers from LGE-CMR is a fundamental step in electrophysiological modeling and cardiovascular disease diagnosis. Deep learning methods have demonstrated extremely promising performance. However, excellent performance often depended on a large amount of finely annotated data. The purpose of this manuscript was to develop a semi-supervised segmentation method to use unlabeled data to improve model performance., Methods: This manuscript proposed a semi-supervised network that integrates triple-consistency constraints (data-level, task-level, and feature-level) for cardiac chambers segmentation from LGE-CMR. Specifically, we designed a network that integrated segmentation and edge prediction tasks based on the mean teacher architecture. This addressed the problem of ignoring some challenging regions because of excluding low-confidence regions of previous research. We also applied a voxel-level contrastive learning strategy to achieve feature-level consistency, helping the model pay attention to the consistency between features overlooked in previous research., Results: In terms of the Dice, Jaccard, Average Surface Distance (ASD), and 95% Hausdorff Distance (95HD) metrics, for the atrial segmentation dataset, the proposed method achieved scores of 88.34%, 79.30%, 7.92, and 2.02 when trained with 10% labeled data, and 90.70%, 83.09%, 6.41, and 1.72 when trained with 20% labeled data. For the ventricular segmentation task, the results were 87.22%, 77.95%, 2.27, and 0.61 with 10% labeled data, and 88.99%, 80.45%, 1.87, and 0.51 with 20% labeled data, respectively., Conclusion: Experiments demonstrated that our method outperforms previous semi-supervised methods, showing the potential of the proposed network for semi-supervised segmentation problems., (© 2024. The Author(s).)

Objective: To investigate the consistency of Chatbot Generative Pretrained Transformer (ChatGPT)-4 in the analysis of clinical pictures of common laryngological conditions., Study Design: Prospective uncontrolled study., Setting: Multicenter study., Methods: Patient history and clinical videolaryngostroboscopic images were presented to ChatGPT-4 for differential diagnoses, management, and treatment(s). ChatGPT-4 responses were assessed by 3 blinded laryngologists with the artificial intelligence performance instrument (AIPI). The complexity of cases and the consistency between practitioners and ChatGPT-4 for interpreting clinical images were evaluated with a 5-point Likert Scale. The intraclass correlation coefficient (ICC) was used to measure the strength of interrater agreement., Results: Forty patients with a mean complexity score of 2.60 ± 1.15. were included. The mean consistency score for ChatGPT-4 image interpretation was 2.46 ± 1.42. ChatGPT-4 perfectly analyzed the clinical images in 6 cases (15%; 5/5), while the consistency between GPT-4 and judges was high in 5 cases (12.5%; 4/5). Judges reported an ICC of 0.965 for the consistency score (P = .001). ChatGPT-4 erroneously documented vocal fold irregularity (mass or lesion), glottic insufficiency, and vocal cord paralysis in 21 (52.5%), 2 (0.05%), and 5 (12.5%) cases, respectively. ChatGPT-4 and practitioners indicated 153 and 63 additional examinations, respectively (P = .001). The ChatGPT-4 primary diagnosis was correct in 20.0% to 25.0% of cases. The clinical image consistency score was significantly associated with the AIPI score (r s  = 0.830; P = .001)., Conclusion: The ChatGPT-4 is more efficient in primary diagnosis, rather than in the image analysis, selecting the most adequate additional examinations and treatments., (© 2024 American Academy of Otolaryngology–Head and Neck Surgery Foundation.)

Objectives: Endovascular thrombectomy (EVT) dramatically improves clinical outcomes, but the reduction in final infarct volume only accounts for 10-15 % of the treatment benefit. We aimed to develop a novel MRI-ADC-based metric that quantify the degree of tissue injury to test the hypothesis that it outperforms infarct volume in predicting long-term outcome., Materials and Methods: A single-center cohort consisted of consecutive acute stroke patients with anterior circulation large vessel occlusion, successful recanalization via EVT (mTICI ≥2b), and MRI of the brain between 12 h and 7 days post-EVT. Imaging was processed via RAPID software. Final infarct volume was based on the traditional ADC <620 threshold. Logistic regression quantified the association of lesion volumes and good outcome (90-day modified Rankin Scale ≤2) at a range of lower ADC thresholds (<570, <520, and <470). Infarct density was calculated as the percentage of the final infarct volume below the ADC threshold with the greatest effect size. Univariate and multivariate logistic regression quantified the association between imaging/clinical metrics and functional outcome., Results: 120 patients underwent MRI after successful EVT. Lesion volume based on the ADC threshold <470 had the strongest association with good outcome (OR: 0.81 per 10 mL; 95 % CI: 0.66-0.99). In a multivariate model, infarct density (<470/<620 * 100) was independently associated with good outcome (aOR 0.68 per 10 %; 95 % CI: 0.49-0.95), but final infarct volume was not (aOR 0.98 per 10 mL; 95 % CI: 0.85-1.14)., Conclusions: Infarct density after EVT is more strongly associated with long-term clinical outcome than infarct volume., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Christopher G. Favilla reports financial support was provided by National Institutes of Health. Christopher G. Favilla reports financial support was provided by American Heart Association Inc., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: The acquisition of contrast-enhanced T1 maps to calculate extracellular volume (ECV) requires contrast agent administration and is time consuming. This study investigates generative adversarial networks for contrast-free, virtual extracellular volume (vECV) by generating virtual contrast-enhanced T1 maps., Methods and Results: This retrospective study includes 2518 registered native and contrast-enhanced T1 maps from 1000 patients who underwent cardiovascular magnetic resonance at 1.5 Tesla. Recent hematocrit values of 123 patients (hold-out test) and 96 patients from a different institution (external evaluation) allowed for calculation of conventional ECV. A generative adversarial network was trained to generate virtual contrast-enhanced T1 maps from native T1 maps for vECV creation. Mean and SD of the difference per patient (ΔECV) were calculated and compared by permutation of the 2-sided t test with 10 000 resamples. For ECV and vECV, differences in area under the receiver operating characteristic curve (AUC) for discriminating hold-out test patients with normal cardiovascular magnetic resonance versus myocarditis or amyloidosis were tested with Delong's test. ECV and vECV showed a high agreement in patients with myocarditis (ΔECV: hold-out test, 2.0%±1.5%; external evaluation, 1.9%±1.7%) and normal cardiovascular magnetic resonance (ΔECV: hold-out test, 1.9%±1.4%; external evaluation, 1.5%±1.2%), but variations in amyloidosis were higher (ΔECV: hold-out test, 6.2%±6.0%; external evaluation, 15.5%±6.4%). In the hold-out test, ECV and vECV had a comparable AUC for the diagnosis of myocarditis (ECV AUC, 0.77 versus vECV AUC, 0.76; P =0.76) and amyloidosis (ECV AUC, 0.99 versus vECV AUC, 0.96; P =0.52)., Conclusions: Generation of vECV on the basis of native T1 maps is feasible. Multicenter training data are required to further enhance generalizability of vECV in amyloidosis.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL) models. A total of 30,080 unique studies were included; 24,013 studies were used to train a convolutional neural network model to automatically assess, at end-diastole, LV internal diameter (LVID), interventricular septal wall thickness (IVS), posterior wall thickness (PWT), and LV mass. The model was trained to select end-diastolic frames with the largest LVID and to identify four landmarks, marking the dimensions of LVID, IVS, and PWT using manually labeled landmarks as reference. The model was validated with 3,014 echocardiographic cines and the accuracy of the model was evaluated with a test set of 3,053 echocardiographic cines. The model accurately measured LVID, IVS, PWT, and LV mass compared to study report values with a mean relative error of 5.40%, 11.73%, 12.76%, and 13.93%, respectively. The 𝑅 2 of the model for the LVID, IVS, PWT, and the LV mass was 0.88, 0.63, 0.50, and 0.87, respectively. The novel DL model developed in this study was accurate for LV dimension assessment without the need to select end-diastolic frames manually. DL automated measurements of IVS and PWT were less accurate with greater wall thickness. Validation studies in larger and more diverse populations are ongoing., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose: Myocardial strain, as a crucial quantitative indicator of myocardial deformation, can detect the changes of cardiac function earlier than parameters such as ejection fraction (EF). It has reported that cardiac magnetic resonance(CMR) and post-processing software possess the ability to obtain the stability and repeatability strain values. Recently, the normal strain values range of people are debatable, especially in the Chinese population. Therefore, we aim to explore the ventricular characteristics and the myocardial strain values of the Chinese people by using the cardiac magnetic resonance feature tracking (CMR-FT). Additionally, we attempted to use the myocardial and chordae tendineae contours to calculate the ventricular volumes by the CMR-FT. This study may provide valuable insights into the application of CMR-FT in tracking the ventricular characteristics and myocardial strain for Chinese population, especially in suggesting an referable myocardial strain parameters of the Chinese., Methods: A total of 109 healthy Chinese individuals (age range: 18 to 58 years; 52 males and 57 females) underwent 3.0T CMR to acquire the cardiac images. The commercial post-processing software was employed to analyse the image sequence by semi-automatic processing, then the biventricular morphology (End-Diastolic Volume, EDV; EDV/Body Surface Area, EDV/BSA), function(EF; Cardiac Output, CO; Cardiac Index, CI) and strain(Radial Strain, RS; Circumferential Strain, CS; Longitudinal Strain, LS) values were obtained.The biventricular myocardial strain values were stratified according to the age and gender. The Left Ventricular( LV base, mid, apex) and myocardial strain values of three coronary artery areas were calculated based on the the strain value of LV American Heart Association(AHA) 16 segments., Results: It was shown that the females had larger LV globe strain values compared with the males (LVGPRS: 42.0 ± 8.5 versus 33.6 ± 6.2%, P < 0.001; LVGPCS: -21.2 ± 2.1 versus - 19.7 ± 2.3%, P < 0.001; LVGPLS: -16.4 ± 2.6 versus - 14.6 ± 2.2%, P < 0.001;). Moreover, the differences in RS, CS, and LS among the LV myocardium 16 segments were obvious. However, the right ventricle (RV) strain values showed non-normal distribution in the volunteers of this research., Conclusions: Here, we successfully tracked the characteristics of bilateral ventricles in healthy Chinese populations through using the 3.0T CMR. We confirmed that there was a gender difference in LV Globe Strain values. In addition, we obtained strain values for each myocardial segment of the LV and different coronary artery regions based on the AHA 16 segments method, Our results also showed that the RV strain values with a non-normal distribution, and RV global strain values were not related to the gender and age. Furthermore, LVGPRS, LVGPLS, and RVGPRS were significantly correlated with BMI, CO, CI, and EDV in the Chinese population., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

Background: New postprocessing software facilitates 3-dimensional (3D) echocardiographic determination of mitral annular (MA) and neo-left ventricular outflow tract (neo-LVOT) dimensions in patients undergoing transcatheter mitral valve replacement (TMVR)., Objectives: This study aims to test the accuracy of 3D echocardiographic analysis as compared to baseline computed tomography (CT)., Methods: A total of 105 consecutive patients who underwent TMVR at 2 tertiary care centers between October 2017 and May 2023 were retrospectively included. A virtual valve was projected in both baseline CT and 3D transesophageal echocardiography (TEE) using dedicated software. MA dimensions were measured in baseline images and neo-LVOT dimensions were measured in baseline and postprocedural images. All measurements were compared to baseline CT as a reference. The predicted neo-LVOT area was correlated with postprocedural peak LVOT gradients., Results: There was no significant bias in baseline neo-LVOT prediction between both imaging modalities. TEE significantly underestimated MA area, perimeter, and medial-lateral dimension compared to CT. Both modalities significantly underestimated the actual neo-LVOT area (mean bias pre/post TEE: 25.6 mm 2 , limit of agreement: -92.2 mm 2 to 143.3 mm 2 ; P < 0.001; mean bias pre/post CT: 28.3 mm 2 , limit of agreement: -65.8 mm 2 to 122.4 mm 2 ; P = 0.046), driven by neo-LVOT underestimation in the group treated with dedicated mitral valve bioprosthesis. Both CT- and TEE-predicted-neo-LVOT areas exhibited an inverse correlation with postprocedural LVOT gradients (r 2  = 0.481; P < 0.001 for TEE and r 2  = 0.401; P < 0.001 for CT)., Conclusions: TEE-derived analysis provides comparable results with CT-derived metrics in predicting the neo-LVOT area and peak gradient after TMVR., Competing Interests: Funding Support and Author Disclosures Dr Bartkowiak has received grants from Novartis Foundation. Dr Windecker has received grants to the institution without personal remuneration from Abbott, Abiomed, Amgen, AstraZeneca, Bayer, Braun, Biotronik, Boehringer Ingelheim, Boston Scientific, Bristol Myers Squibb, Cardinal Health, CardioValve, Cordis Medical, Corflow Therapeutics, CSL Behring, Daiichi Sankyo, Edwards Lifesciences, Farapulse Inc, Fumedica, Guerbet, Idorsia, Inari Medical, InfraRedx, Janssen-Cilag, Johnson and Johnson, Medalliance, Medicure, Medtronic, Merck Sharp and Dohm, Miracor Medical, MonarQ, Novartis, Novo Nordisk, Organon, OrPha Suisse, Pharming Tech, Pfizer, Polares, Regeneron, Sanofi-Aventis, Servier, Sinomed, Terumo, Vifor, and V-Wave; has served as advisory board member and/or member of the steering/executive group of trials funded by Abbott, Abiomed, Amgen, AstraZeneca, Bayer, Boston Scientific, Biotronik, Bristol Myers Squibb, Edwards Lifesciences, MedAlliance, Medtronic, Novartis, Polares, Recardio, Sinomed, Terumo, and V-Wave with payments to the institution but no personal payments; and has been a member of a steering/executive committee group of several investigator-initiated trials that receive funding by industry without impact on his personal remuneration. Dr George has received consulting fees from Zimmer Biomet, Atricure, Neosurgery, Neptune Medical, Abbvie, Johnson and Johnson, Durvena, Boston Scientific, Edwards Lifesciences, Medtronic, Help-TheraX, 3ive, Encompass, Summus Medical, Abbott SJM, BCI, and Xeltis; has been on advisory boards for Edwards Surgical, Medtronic Surgical, Medtronic Structural Mitral and Tricuspid, Trisol Medical, Valcare Medical, Durvena, Abbvie, Johnson and Johnson, Foldax Medical, Zimmer Biomet, Neosurgery, Abbvie, Boston Scientific, Summus Medical, BCI Equity: Valcare Medical, Durvena, CardioMech, Vdyne, MitreMedical, MITRx, and BCI; and has received institutional funding to Columbia University from Edwards Lifesciences, Medtronic, Abbott Vascular, Boston Scientific, and JenaValve. Dr Kodali has received institutional grants from Edwards Lifesciences, Medtronic, and Abbott; has received consulting fees from Abbott, Admedus, and Meril Lifesciences; and has equity options from Biotrace Medical and Thubrikar Aortic Valve Inc. Dr Praz has been compensated for travel expenses from Edwards Lifescicences, Abbott Vascular, Medira, Polares Medical, and Siemens Healthineers. Dr Hahn has received speaker fees from Abbott Structural, Baylis Medical, Edwards Lifesciences, Medtronic, and Philips Healthcare; has institutional consulting contracts for which she receives no direct compensation with Abbott Structural, Edwards Lifesciences, Medtronic, and Novartis; and the is Chief Scientific Officer for the Echocardiography Core Laboratory at the Cardiovascular Research Foundation for multiple industry-sponsored tricuspid valve trials for which she receives no direct industry compensation. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Purpose: Visual interpretation of brain amyloid-β (Aβ) PET can be difficult in individuals with borderline Aβ burden. Coregistration with individual MRI is recommended in these cases, which, however, is not always available. This study evaluated coregistration with the early perfusion frames acquired immediately after tracer injection to support the visual interpretation of the late Aβ-frames in PET with 18F-flutemetamol (FMM)., Patients and Methods: Fifty dual-time-window FMM-PET scans of cognitively normal subjects with 0 to 60 Centiloids were included retrospectively (70.1 ± 6.9 years, 56% female, MMSE score 28.9 ± 1.3, 42% APOE ɛ4 carrier). Regional Aβ load was scored with respect to a 6-point Likert scale by 3 independent raters in the 10 regions of interest recommended for FMM reading using 3 different settings: Aβ image only, Aβ image coregistered with MRI, and Aβ image coregistered with the perfusion image. The impact of setting, within- and between-readers variability, region of interest, and Aβ-status was tested by repeated-measure analysis of variance of the Likert score., Results: The Centiloid scale ranged between 2 and 52 (interquartile range, 7-19). Support of visual scoring by the perfusion image resulted in the best discrimination between Aβ-positive and Aβ-negative cases, mainly by improved certainty of excluding Aβ plaques in Aβ-negative cases (P = 0.030). It also resulted in significantly higher between-rater agreement. The setting effect was most pronounced in the frontal lobe and in the posterior cingulate cortex/precuneus area (P = 0.005)., Conclusions: The early perfusion image is a suitable alternative to T1-weighted MRI to support the visual interpretation of the late Aβ image in FMM-PET., Competing Interests: Conflicts of interest and sources of funding: L.E.C. received research support from GE Healthcare and Spinger Healthcare (funded by Eli Lilly). Both contributions have been paid to the institution. There is no actual or potential conflict of interest for the other authors., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology., Competing Interests: Declaration of competing interest none., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Competing Interests: Dr Zhang has authorship rights for patent WO2021044153: Enhancement of Medical Images. W.Y.R. Fok reports no conflicts.

Colon capsule endoscopy (CCE) is a well-known method for the detection of colorectal lesions. Nevertheless, there are no studies reporting the accuracy of TOP 100, a CCE software tool, for the automatic detection of colorectal lesions in CCE. We aimed to evaluate the performance of TOP 100 in detecting colorectal lesions in patients submitted to CCE for incomplete colonoscopy compared with classic reading. A retrospective cohort study including adult patients submitted to CCE (PillCam COLON 2; Medtronic) for incomplete colonoscopy. Blinded for each other's evaluation, one experienced reader analyzed the TOP 100 images and the other performed classic reading to identify colorectal lesions. Detection of colorectal lesions, namely polyps, angioectasia, blood, diverticula, erosions/ulcers, neoplasia, and subepithelial lesions was assessed and TOP 100 performance was evaluated compared with the gold standard (classic reading). A total of 188 CCEs were included. Prevalence of colorectal lesions, polyps, angioectasia, blood, diverticula, erosions/ulcers, neoplasia, and subepithelial lesions were 77.7, 54.3, 8.5, 1.6, 50.0, 0.5, 0.5, and 1.1%, respectively. TOP 100 had a sensitivity of 92.5%, specificity of 69.1%, negative predictive value of 72.5%, positive predictive value of 91.2%, and accuracy of 87.2% for detecting colorectal lesions. TOP 100 had a sensitivity of 89.2%, specificity of 84.9%, negative predictive value of 86.9%, positive predictive value of 87.5%, and accuracy of 87.2% in detecting polyps. All colorectal lesions other than polyps were identified with 100% accuracy by TOP 100. TOP 100 has been shown to be a simple and useful tool in assisting the reader in the prompt identification of colorectal lesions in CCE., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

To evaluate a convolutional neural network's performance (nnU-Net) in the assessment of vascular contours, calcification and PET tracer activity using Ga-68 DOTATATE PET/CT. Patients who underwent Ga-68 DOTATATE PET/CT imaging over a 12-month period for neuroendocrine investigation were included. Manual cardiac and aortic segmentations were performed by an experienced observer. Scans were randomly allocated in ratio 64:16:20 for training, validation and testing of the nnU-Net model. PET tracer uptake and calcium scoring were compared between segmentation methods and different observers. 116 patients (53.5% female) with a median age of 64.5 years (range 23-79) were included. There were strong, positive correlations between all segmentations (mostly r > 0.98). There were no significant differences between manual and AI segmentation of SUV mean for global cardiac (mean ± SD 0.71 ± 0.22 vs. 0.71 ± 0.22; mean diff 0.001 ± 0.008, p > 0.05), ascending aorta (mean ± SD 0.44 ± 0.14 vs. 0.44 ± 0.14; mean diff 0.002 ± 0.01, p > 0.05), aortic arch (mean ± SD 0.44 ± 0.10 vs. 0.43 ± 0.10; mean diff 0.008 ± 0.16, p > 0.05) and descending aorta (mean ± SD < 0.001; 0.58 ± 0.12 vs. 0.57 ± 0.12; mean diff 0.01 ± 0.03, p > 0.05) contours. There was excellent agreement between the majority of manual and AI segmentation measures (r ≥ 0.80) and in all vascular contour calcium scores. Compared with the manual segmentation approach, the CNN required a significantly lower workflow time. AI segmentation of vascular contours using nnU-Net resulted in very similar measures of PET tracer uptake and vascular calcification when compared to an experienced observer and significantly reduced workflow time., (© 2024. Crown.)

Global longitudinal strain (GLS) is an echocardiographic measure to detect chemotherapy-related cardiovascular dysfunction. However, its limited availability and the needed expertise may restrict its generalization. Artificial intelligence (AI)-based GLS might overcome these challenges. Our aims are to explore the agreements between AI-based GLS and conventional GLS, and to assess whether the agreements were influenced by expertise levels, cardiac remodeling and cardiovascular diseases/risks. Echocardiographic images in the apical four-chamber view of left ventricle were retrospectively analyzed based on AI-based GLS in patients treated with chemotherapy, and correlation between AI-based GLS (Caas Qardia, Pie Medical Imaging) and conventional GLS (Vivid E9/VividE95, GE Healthcare) were assessed. The agreement between unexperienced physicians ("GLS beginner") and experienced echocardiographer were also assessed. Among 94 patients (mean age 69 ± 12 years, 73% female), mean left ventricular ejection fraction was 64 ± 6%, 14% of patients had left ventricular hypertrophy, and 21% had left atrial enlargement. Mean GLS was - 15.9 ± 3.4% and - 19.0 ± 3.7% for the AI and conventional method, respectively. There was a moderate correlation between these methods (rho = 0.74; p < 0.01), and bias was - 3.1% (95% limits of agreement: -8.1 to 2.0). The reproducibility between GLS beginner and an experienced echocardiographer was numerically better in the AI method than the conventional method (inter-observer agreement = 0.82 vs. 0.68). The agreements were consistent across abnormal cardiac structure and function categories (p-for-interaction > 0.10). In patients treated with chemotherapy. AI-based GLS was moderately correlated with conventional GLS and provided a numerically better reproducibility compared with conventional GLS, regardless of different levels of expertise., (© 2024. The Author(s).)

Neoadjuvant therapy (NAT) has become routine in patients with borderline resectable pancreatic cancer. Pathologists examine pancreatic cancer resection specimens to evaluate the effect of NAT. However, an automated scoring system to objectively quantify residual pancreatic cancer (RPC) is currently lacking. Herein, we developed and validated the first automated segmentation model using artificial intelligence techniques to objectively quantify RPC. Digitized histopathological tissue slides were included from resected pancreatic cancer specimens from 14 centers in 7 countries in Europe, North America, Australia, and Asia. Four different scanner types were used: Philips (56%), Hamamatsu (27%), 3DHistech (10%), and Leica (7%). Regions of interest were annotated and classified as cancer, non-neoplastic pancreatic ducts, and others. A U-Net model was trained to detect RPC. Validation consisted of by-scanner internal-external cross-validation. Overall, 528 unique hematoxylin and eosin (H & E) slides from 528 patients were included. In the individual Philips, Hamamatsu, 3DHistech, and Leica scanner cross-validations, mean F1 scores of 0.81 (95% CI, 0.77-0.84), 0.80 (0.78-0.83), 0.76 (0.65-0.78), and 0.71 (0.65-0.78) were achieved, respectively. In the meta-analysis of the cross-validations, the mean F1 score was 0.78 (0.71-0.84). A final model was trained on the entire data set. This ISGPP model is the first segmentation model using artificial intelligence techniques to objectively quantify RPC following NAT. The internally-externally cross-validated model in this study demonstrated robust performance in detecting RPC in specimens. The ISGPP model, now made publically available, enables automated RPC segmentation and forms the basis for objective NAT response evaluation in pancreatic cancer., Competing Interests: Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Computational fluid dynamics (CFD) was used to identify factors influencing the accuracy of the hemispherical proximal isovelocity surface area (PISA) method in calculating the effective regurgitant orifice area (EROA) for patients with functional mitral regurgitation (FMR). Ninety-nine CFD models were constructed to investigate the impact of regurgitant orifice shape and leaflet tethering on the EROA calculation using the PISA method. The correction factors for regurgitation orifice shape (CFs) and for leaflet tethering (CFt) were derived by comparing the 2D PISA method and the actual orifice area. The correction formula was then tested in vivo via 2D transthoracic echocardiography with 3D transesophageal echocardiography of the vena contracta area (VCA) as a reference method in 62 patients with FMR. Based on the CFD simulation results, the two major factors for correcting the EROA calculation were vena contracta length (VCL) and coaptation depth (CD). The correction formula for the EROA was corrected effective regurgitant orifice area (CEROA) = EROA*CFs*CFt, where CFs = 0.59 × VCL(cm) + 0.6 × MR Vmax(cm/s)-0.63 × PISA R(cm)-1.51 and CFt = 0.4 × CD (cm) + 0.96. The correction formula was applied to FMR patients, and the bias and LOA between the CEROA and VCA (0.01 ± 0.13 cm 2 ) were much smaller than those between the EROA and VCA (0.26 ± 0.32 cm 2 ). The CFD-based correction formula improves the accuracy of the EROA calculation based on the hemispheric PISA method, possibly leading to more accurate and reliable data for treatment decision-making in FMR patients., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)