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Women's engagement in medicine, and more specifically cardiovascular imaging and cardiovascular MRI (CMR), has undergone a slow evolution over the past several decades. As a result, an increasing number of women have joined the cardiovascular imaging community to contribute their expertise. This collaborative work summarizes the barriers that women in cardiovascular imaging have overcome over the past several years, the positive interventions that have been implemented to better support women in the field of CMR, and the challenges that still remain, with a special emphasis on women physicians.

Objectives: The aim of this study was to compare the costs of a noninvasive cardiac magnetic resonance (CMR)–guided strategy versus 2 invasive strategies with and without fractional flow reserve (FFR). Background: Coronary artery disease (CAD) is a major contributor to the public health burden. Stress perfusion CMR has excellent accuracy to detect CAD. International guidelines recommend as a first step noninvasive testing of patients in stable condition with known or suspected CAD. However, nonadherence in routine clinical practice is high. Methods: In the EuroCMR (European Cardiovascular Magnetic Resonance) registry (n = 3,647, 59 centers, 18 countries) and the U.S.-based SPINS (Stress-CMR Perfusion Imaging in the United States) registry (n = 2,349, 13 centers, 11 states), costs were calculated for 12 health care systems (8 in Europe, the United States, 2 in Latin America, and 1 in Asia). Costs included diagnostic examinations (CMR and x-ray coronary angiography [CXA] with and without FFR), revascularizations, and complications during 1-year follow-up. Seven subgroup analyses covered low- to high-risk cohorts. Patients with ischemia-positive CMR underwent CXA and revascularization at the treating physician's discretion (CMR+CXA strategy). In the hypothetical invasive CXA+FFR strategy, costs were calculated for initial CXA and FFR in vessels with ≥50% stenoses, assuming the same proportion of revascularizations and complications as with the CMR+CXA strategy and FFR-positive rates as given in the published research. In the CXA-only strategy, costs included CXA and revascularizations of ≥50% stenoses. Results: Consistent cost savings were observed for the CMR+CXA strategy compared with the CXA+FFR strategy in all 12 health care systems, ranging from 42% ± 20% and 52% ± 15% in low-risk EuroCMR and SPINS patients with atypical chest pain, respectively, to 31% ± 16% in high-risk SPINS patients with known CAD (P < 0.0001 vs 0 in all groups). Cost savings were even higher compared with CXA only, at 63% ± 11%, 73% ± 6%, and 52% ± 9%, respectively (P < 0.0001 vs 0 in all groups). Conclusions: In 12 health care systems, a CMR+CXA strategy yielded consistent moderate to high cost savings compared with a hypothetical CXA+FFR strategy over the entire spectrum of risk. Cost savings were consistently high compared with CXA only for all risk groups. © 2022 The Authors

Microvascular wall abnormalities demonstrated by nailfold capillaroscopy in systemic sclerosis (SSc) may result in microhemorrhagic deposition of erythrocyte-derived iron. Such abnormalities precede fibrosis, which is orchestrated by myofibroblasts. Iron induces endothelial-to-mesenchymal transition in vitro, which is reversed by reactive oxygen species (ROS) scavengers. The conversion of quiescent fibroblasts into profibrotic myofibroblasts has also been associated with ROS-mediated activation of TGF-β1. Given that iron overload predisposes to ROS formation, we hypothesized that the uptake of erythrocyte-derived iron by resident cells promotes fibrosis. Firstly, we show that iron induces oxidative stress in skin-derived and synovial fibroblasts in vitro, as well as in blood mononuclear cells ex vivo. The biological relevance of increased oxidative stress was confirmed by showing the concomitant induction of DNA damage in these cell types. Similar results were obtained in vivo, following intravenous iron administration. Secondly, using magnetic resonance imaging we show an increased iron deposition in the fingers of a patient with early SSc and nailfold microhemorrhages. While a systematic magnetic resonance study to examine tissue iron levels in SSc, including internal organs, is underway, herein we propose that iron may be a pathogenetic link between microvasculopathy and fibrosis and an additional mechanism responsible for increased oxidative stress in SSc. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

A 30-year-old man with a history of an in-situ melanoma of the forehead was referred for cardiac evaluation because of tachycardia and elevated levels of serum troponin. The transthoracic echocardiogram revealed multiple masses attached to the walls of both ventricles and the right atrium (RA). A large mass was occupying almost one third of the right ventricle (RV), resulting in reduction of the end-diastolic RV volume and tachycardia. A cardiac magnetic resonance imaging confirmed multifocal myocardial infiltration and intracavitary masses and excluded the presence of thrombus in any of the cardiac chambers. Diffuse metastatic involvement in the liver, the spleen, and the brain by computed tomography precluded surgical management. Being BRAF-unmutated, the patient was initially treated with a combination of nivolumab and ipilimumab. One month later, the cardiac metastases in RA and left ventricle were unchanged on echocardiogram, while the tumor in RV was enlarged occupying the majority of the chamber, resulting in further reduction of the cardiac output and tachycardia. The treatment was changed to a combination of dacarbazine and carboplatin, but the patient eventually died two months later. Heart is not a common metastatic site of melanoma and cardiac involvement is usually clinically silent making ante mortem diagnosis difficult. Multimodalidy imaging plays a pivotal role in the diagnostic work up. Cardiac melanoma metastases indicate an advance stage disease with poor prognosis. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Hypertension is the most common causative factor of cardiac remodeling, which, in turn, has been associated with changes in brain and kidney function. Currently, the role of blood biomarkers as indices of cardiac remodeling remains unclear. In contrast, cardiac imaging, including echocardiography and cardiovascular magnetic resonance (CMR), has been a valuable noninvasive tool to assess cardiac remodeling. Cardiac remodeling during the course of systemic hypertension is not the sole effect of the latter. Remodeling of other vital organs, such as brain and kidney, also takes place. Therefore, it will be more accurate if we discuss about hypertensive remodeling involving the heart, the brain, and the kidneys, rather than isolated cardiac remodeling. This supports the idea of their simultaneous assessment to identify the early, silent lesions of total hypertensive remodeling. In this context, magnetic resonance imaging is the ideal modality to provide useful information about these organs in a noninvasive fashion and without radiation. For this purpose, we propose a combined protocol to employ MRI in the simultaneous assessment of the heart, brain and kidneys. This protocol should include all necessary indices for the evaluation of hypertensive remodeling in these 3 organs, and could be performed within a reasonable time, not exceeding one hour, so that it remains patient-friendly. Furthermore, a combined protocol may offer all in one examination and save time. Finally, the amount of contrast agent used will be limited granted that post-contrast evaluations of the three organs will be performed after 1 injection. © 2022 BMJ Publishing Group. All rights reserved.

Background: Human immunodeficiency virus (HIV) is mainly detected in young, otherwise healthy, individuals. Cardiomyopathy and peripheral artery disease affecting these patients appears to be multifactorial. Prompt and potentially more effective implementation of therapeutic measures could be enabled by pre‐symptomatic diagnosis of myocardial dysfunction and peripheral artery damage. However, limited data is available to date on this specific topic. Μethods: We investigated the association between global longitudinal strain (GLS), an established index of subclinical left ventricular systolic dysfunction (LVSD) assessed by two‐dimensional speckle‐tracking echocardiography, and: (a) patient history; (b) demographic and clinical baseline characteristics; (c) carotid intima‐media thickness (IMT) and the presence of carotid atherosclerotic plaque(s), measured by ultrasonography; (d) temperature difference (ΔT) along each carotid artery, measured by microwave radiometry; and (e) basic blood panel measurements, including high-sensitivity troponin‐T (hsTnT) and NT‐proBNP in people living with HIV (PLWH) and no history of cardiovascular disease. Results: We prospectively enrolled 103 consecutive PLWH (95% male, age 47 ± 11 years, anti‐retroviral therapy 100%) and 52 age‐ and sex‐matched controls. PLWH had a significantly higher relative wall thickness (0.38 ± 0.08 vs. 0.36 ± 0.04, p = 0.048), and higher rate of LVSD (34% vs. 15.4%, p = 0.015), and carotid artery atherosclerosis (28% vs. 6%, p = 0.001) compared with controls. Among PLWH, LVSD was independently associated with the presence of carotid atherosclerosis (adj. OR:3.09; 95%CI:1.10–8.67, p = 0.032) and BMI (1.15; 1.03–1.29, p = 0.017), while a trend for association between LVSD and left ventricular hypertrophy was also noted (3.12; 0.73– 13.33, p = 0.124). No differences were seen in microwave radiometry parameters, NT‐proBNP, hs‐ TnT and c‐reactive protein between PLWH with and without LVSD. Conclusions: Subclinical LVSD and carotid atherosclerosis were significantly more frequent in PLWH compared to a group of healthy individuals, implying a possible link between HIV infection and these two pathological processes. Carotid atherosclerosis and increased adiposity were independently associated with impaired GLS in HIV‐infected individuals. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

This document is a position statement from the Society for Cardiovascular Magnetic Resonance (SCMR) on recommendations for clinical utilization of cardiovascular magnetic resonance (CMR) in women with cardiovascular disease. The document was prepared by the SCMR Consensus Group on CMR Imaging for Female Patients with Cardiovascular Disease and endorsed by the SCMR Publications Committee and SCMR Executive Committee. The goals of this document are to (1) guide the informed selection of cardiovascular imaging methods, (2) inform clinical decision-making, (3) educate stakeholders on the advantages of CMR in specific clinical scenarios, and (4) empower patients with clinical evidence to participate in their clinical care. The statements of clinical utility presented in the current document pertain to the following clinical scenarios: acute coronary syndrome, stable ischemic heart disease, peripartum cardiomyopathy, cancer therapy-related cardiac dysfunction, aortic syndrome and congenital heart disease in pregnancy, bicuspid aortic valve and aortopathies, systemic rheumatic diseases and collagen vascular disorders, and cardiomyopathy-causing mutations. The authors cite published evidence when available and provide expert consensus otherwise. Most of the evidence available pertains to translational studies involving subjects of both sexes. However, the authors have prioritized review of data obtained from female patients, and direct comparison of CMR between women and men. This position statement does not consider CMR accessibility or availability of local expertise, but instead highlights the optimal utilization of CMR in women with known or suspected cardiovascular disease. Finally, the ultimate goal of this position statement is to improve the health of female patients with cardiovascular disease by providing specific recommendations on the use of CMR.

Wilson’s disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a “conventional deep convolution neural network” (cDCNN) and an “improved DCNN” (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring “differentiable at zero.” Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning–based “Inception V3” paradigm by 11.92% and (b) four types of “conventional machine learning–based systems”: k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. [Figure not available: see fulltext.]. © 2021, International Federation for Medical and Biological Engineering.

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from “ground truth” images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound. © 2021, Society for Imaging Informatics in Medicine.

Increased cardiac fat depots are metabolically active tissues that have a pronounced pro-inflammatory nature. Increasing evidence supports a potential role of cardiac adiposity as a determinant of the substrate of atrial fibrillation and ventricular arrhythmias. The underlying mecha-nism appears to be multifactorial with local inflammation, fibrosis, adipocyte infiltration, electrical remodeling, autonomic nervous system modulation, oxidative stress and gene expression playing interrelating roles. Current imaging modalities, such as echocardiography, computed tomography and cardiac magnetic resonance, have provided valuable insight into the relationship between cardiac adiposity and arrhythmogenesis, in order to better understand the pathophysiology and improve risk prediction of the patients, over the presence of obesity and traditional risk factors. However, at present, given the insufficient data for the additive value of imaging biomarkers on commonly used risk algorithms, the use of different screening modalities currently is indicated for personalized risk stratification and prognostication in this setting. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD. © 2020 EDIZIONI MINERVA MEDICA

Cardiovascular Magnetic Resonance (CMR) offers accurate and highly reproducible tissue characterization, beyond cardiac function. Late gadolinium enhancement (LGE), although represents a noninvasive biopsy for fibrosis quantification, it is unable to detect diffuse myocardial disease. Native T1 mapping and extracellular volume fraction (ECV) are able to provide important information about processes involving both myocardial cells and interstitium that otherwise cannot be identified. Changes in myocardial native T1 mapping reflect cardiac diseases such as acute coronary syndromes, myocardial infarction, myocarditis, diffuse fibrosis, systemic disease such as cardiac amyloidosis, all presented with high T1 and Anderson-Fabry disease and siderosis, presented with low T1 mapping. The ECV, an index generated by native and postcontrast T1 mapping, introduces a new way to measure the cellular and extracellular interstitial matrix (ECM). ECV has a prognostic value equal to Left ventricular ejection fraction (LVEF); however, LVEF underscores the interstitial matrix. This myocyte-ECM dichotomy has important implications for identifying therapeutic targets that are of great value for heart failure (HF) treatment. Furthermore, T2 mapping is superior compared with myocardial T1 and ECM for assessing the activity of myocarditis in recent-onset HF. These indices will affect significantly the clinical decision making. However, there is still lack of multicenter studies and community-wide approach including MRI vendors, clinicians, fundings, softwares, and contrast agent manufacturers. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Background: Heterozygous familial hypercholesterolemia (heFH) is a genetic disorder leading to premature coronary artery disease (CAD). We hypothesized that the subclinical pathophysiologic consequences of hypercholesterolemia may be detected before the occurrence of clinically overt CAD by stress testing and myocardial strain imaging. Patients-methods: We evaluated the treadmill tests (ETTs) of 46 heFH men without known arterial hypertension/diabetes mellitus/vasculopathy like CAD and of 39 healthy men matched for age, baseline systolic/diastolic blood pressure (BP) and heart rate (HR), using Bruce protocol. Global longitudinal strain (GLS) of the left ventricle (LV) additionally to ejection fraction was obtained. Results: heFH men reached a significantly higher peak systolic and diastolic BP compared to controls (p = 0.002 and p < 0.001, respectively). Mean rate pressure product was significantly higher in heFH patients (p = 0.038). Both duration of the ETT and workload in metabolic equivalents was lower in the heFH group (p < 0.001 and p < 0.001, respectively). Baseline to peak rise of systolic and diastolic BP in heFH men was higher (p = 0.008 and p < 0.001 for systolic and diastolic BP, respectively). Furthermore, heFH men had higher rise of HR from baseline to peak, compared to controls; (p = 0.047). GLS in heHF men was slightly decreased (p = 0.014), although the ejection fraction was similar in both groups. Conclusion: heFH men have a higher rise in systolic/diastolic BP during ETT, which may reflect early, preclinical hypertension. Furthermore, slight impairment of LV GLS is present, despite the absence of apparent myocardial dysfunction in conventional 2D echocardiography. © 2021 The Authors

This document is a position statement from the Society for Cardiovascular Magnetic Resonance (SCMR) on recommendations for clinical utilization of cardiovascular magnetic resonance (CMR) in women with cardiovascular disease. The document was prepared by the SCMR Consensus Group on CMR Imaging for Female Patients with Cardiovascular Disease and endorsed by the SCMR Publications Committee and SCMR Executive Committee. The goals of this document are to (1) guide the informed selection of cardiovascular imaging methods, (2) inform clinical decision-making, (3) educate stakeholders on the advantages of CMR in specific clinical scenarios, and (4) empower patients with clinical evidence to participate in their clinical care. The statements of clinical utility presented in the current document pertain to the following clinical scenarios: acute coronary syndrome, stable ischemic heart disease, peripartum cardiomyopathy, cancer therapy-related cardiac dysfunction, aortic syndrome and congenital heart disease in pregnancy, bicuspid aortic valve and aortopathies, systemic rheumatic diseases and collagen vascular disorders, and cardiomyopathy-causing mutations. The authors cite published evidence when available and provide expert consensus otherwise. Most of the evidence available pertains to translational studies involving subjects of both sexes. However, the authors have prioritized review of data obtained from female patients, and direct comparison of CMR between women and men. This position statement does not consider CMR accessibility or availability of local expertise, but instead highlights the optimal utilization of CMR in women with known or suspected cardiovascular disease. Finally, the ultimate goal of this position statement is to improve the health of female patients with cardiovascular disease by providing specific recommendations on the use of CMR. © 2021, The Author(s).

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings. © 2021 Elsevier Ltd

Introduction: Primary heart involvement in systemic sclerosis may cause morpho-functional and electrical cardiac abnormalities and is a common cause of death. The absence of a clear definition of primary heart involvement in systemic sclerosis limits our understanding and ability to focus on clinical research. We aimed to create an expert consensus definition for primary heart involvement in systemic sclerosis. Methods: A systematic literature review of cardiac involvement and manifestations in systemic sclerosis was conducted to inform an international and multi-disciplinary task force. In addition, the nominal group technique was used to derive a definition that was then subject to voting. A total of 16 clinical cases were evaluated to test face validity, feasibility, reliability and criterion validity of the newly created definition. Results: In total, 171 publications met eligibility criteria. Using the nominal group technique, experts added their opinion, provided statements to consider and ranked them to create the consensus definition, which received 100% agreement on face validity. A median 60(5–300) seconds was taken for the feasibility on a single case. Inter-rater agreement was moderate (mKappa (95% CI) = 0.56 (0.46–1.00) for the first round and 0.55 (0.44–1.00) for the second round) and intra-rater agreement was good (mKappa (95% CI) = 0.77 (0.47–1.00)). Criterion validity showed a 78 (73–84)% correctness versus gold standard. Conclusion: A preliminary primary heart involvement in systemic sclerosis consensus-based definition was created and partially validated, for use in future clinical research. © The Author(s) 2021.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment. © 2021 The Author(s). Published by BRI.

The presentation and identification of cardiovascular disease in women pose unique diagnostic challenges compared to men, and underrecognized conditions in this patient population may lead to clinical mismanagement.This article reviews the sex differences in cardiovascular disease, explores the diagnostic and prognostic role of cardiovascular magnetic resonance (CMR) in the spectrum of cardiovascular disorders in women, and proposes the added value of CMR compared to other imaging modalities. In addition, this article specifically reviews the role of CMR in cardiovascular diseases occurring more frequently or exclusively in female patients, including Takotsubo cardiomyopathy, connective tissue disorders, primary pulmonary arterial hypertension and peripartum cardiomyopathy. Gaps in knowledge and opportunities for further investigation of sex-specific cardiovascular differences by CMR are also highlighted.

Background: Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA. Methods: The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin. Results: The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%. © Cardiovascular Diagnosis and Therapy. All rights reserved.

Background: Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment. Methods: The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC). Results: South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m2). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P

Recent findings: Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called “integrated predictive CVD risk models. Purpose: of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework. Conclusion: Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies. © 2020 Elsevier Ltd

The prevention of sudden cardiac death (SCD) in cardiomyopathies (CM) remains a challenge. The current guidelines still favor the implantation of devices for the primary prevention of SCD only in patients with severely reduced left ventricular ejection fraction (LVEF) and heart failure (HF) symptoms. The implantation of an implantable cardioverter-defibrillator (ICD) is a protective barrier against arrhythmic events in CMs, but the benefit does not outweigh the cost in low risk patients. The identification of high risk patients is the key to an individualized prevention strategy. Cardiac magnetic resonance (CMR) provides reliable and reproducible information about biventricular function and tissue characterization. Furthermore, late gadolinium enhancement (LGE) quantification and pattern of distribution, as well as abnormal T1 mapping and extracellular volume (ECV), representing indices of diffuse fibrosis, can enhance our ability to detect high risk patients. CMR can also complement electro-anatomical mapping (EAM), a technique already applied in the risk evaluation and in the ventricular arrhythmias ablation therapy of CM patients, providing a more accurate assessment of fibrosis and arrhythmic corridors. As a result, CMR provides a new insight into the pathological substrate of CM. CMR may help identify high risk CM patients and, combined with EAM, can provide an integrated evaluation of scar and arrhythmic corridors in the ablative therapy of ventricular arrhythmias. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Motivation: The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA). Method: The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media-adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement. Results: Using the database of 250 carotid scans, the cIMT error using the AI model is 0.0935±0.0637 mm, which is lower than those of all previous methods. The PA error is found to be 2.7939±2.3702 mm2. The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001). Conclusion: A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans. © 2020 Elsevier Ltd

Background Friedreich's ataxia (FA) is an autosomal-recessive neurodegenerative disease characterised by neurologic, cardiac and endocrine abnormalities. Currently, Friedreich cardiomyopathy (FA-CM) staging is based on early ECG findings, high sensitivity troponin (hsTNT) ≥14 ng/ml and echocardiographic left ventricular (LV) morphologic and functional evaluation. However, further parameters, accessible only by cardiovascular magnetic resonance (CMR), such as myocardial oedema, perfusion defects, replacement and/or diffuse myocardial fibrosis, may have a place in the staging of FA-CA. Our aim was to elucidate the additive value of CMR in FA-CM. Methods Three FA cases were assessed using ECG, 24 h Holter recording, hsTNT, routine ECHO including wall dimension, valvular and ventricular function evaluation and CMR using 1.5T Ingenia system. Ventricular volumes-function, wall dimensions and fibrosis imaging using late gadolinium enhancement (LGE) was performed. Results All FA patients had non-specific ECG changes, almost normal 24 h Holter recording, mild hypertrophy with normal function assessed by echocardiography and increased hsTNT. However, the CMR evaluation revealed the presence of LGE >5% of LV mass, indicative of severe fibrosis. Therefore, the FA patients were re-categorized as having severe FA-CA, although their LVEF remained normal. Conclusion The combination of classical diagnostic indices and CMR may reveal early asymptomatic FA-CM and motivate the early initiation of cardiac treatment. Furthermore, these indices can be also used to validate specific treatment targets in FA, potentially useful in the prevention of FA-CM. © 2020 - IOS Press and the authors. All rights reserved.

Takotsubo syndrome (TTS) is an acute and mostly reversible cardiomyopathy that mimics an acute coronary syndrome with left ventricular (LV) systolic dysfunction without relevant obstructive coronary artery disease. Its prevalence is probably underestimated and reaches 1.2–2% in patients with acute coronary syndrome undergoing coronary catheterization. Although supraphysiological epinephrine levels have been associated with TTS, the detailed pathophysiology is incompletely understood. Chest pain is the most common clinical presentation; however, cardiac decompensation, cardiogenic shock, and sudden cardiac death due to ventricular fibrillation may also be the first clinical manifestations. Patients are mostly postmenopausal women, in whom the condition is commonly associated with emotional triggers; however, men have a higher prevalence of TTS being associated with physical triggers, which has a worse prognosis compared with TTS associated with emotional triggers. As a diagnosis of exclusion, TTS has no single definitive diagnostic test. According to the distribution of LV wall motion abnormalities, various morphological subtypes have been identified. The final diagnosis depends on cardiac imaging with left ventricular angiography during acute heart catheterization, as well as on echocardiography and cardiac magnetic resonance. Most patients recover completely, albeit several factors have been associated with worse prognosis. Management is based on observational data, while randomized multicenter studies are still lacking. This review provides a general overview of TTS and focuses on the hypothesized pathophysiology, and especially on current practices in diagnosis, prognosis, and treatment. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

The objectives of this study are to (1) examine the “10-year cardiovascular risk” in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called “AtheroEdge Composite Risk Score 2.0” (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against “conventional cardiovascular risk calculators.” These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian–Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% (P '.0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P =.0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P =.06). © The Author(s) 2020.

We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima–media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females −0.25 (P

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors. © 2020 Suri et al. Published by IMR Press.

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer-aided design-based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%, 0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML-based soft classifier - Random Forest. © The Institution of Engineering and Technology 2020

Background: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRCML) and compares against statistically derived CVRC (CVRCStat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors). Methods: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRCStat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRCStat, the patients were again risk-stratified using CVRCML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events. Results: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRCStat calculators were: AECRS2.0 (AUC 0.83, P

The perception that women represent a low-risk population for cardiovascular (CV) disease (CVD) needs to be reconsidered. Starting from risk factors, women are more likely to be susceptible to unhealthy behaviors and risk factors that have different impact on CV morbidity and mortality as compared to men. Despite the large body of evidence as regards the effect of lifestyle factors on the CVD onset, the gender-specific effect of traditional and non-traditional risk factors on the prognosis of patients with already established CVD has not been well investigated and understood. Furthermore, CVD in women is often misdiagnosed, underestimated, and undertreated. Women also experience hormonal changes from adolescence till elder life that affect CV physiology. Unfortunately, in most of the clinical trials women are underrepresented, leading to the limited knowledge of CV and systemic impact effects of several treatment modalities on women's health. Thus, in this consensus, a group of female cardiologists from the Hellenic Society of Cardiology presents the special features of CVD in women: the different needs in primary and secondary prevention, as well as therapeutic strategies that may be implemented in daily clinical practice to eliminate underestimation and undertreatment of CVD in the female population. © 2020 Hellenic Society of Cardiology

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high-moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image-based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment. © 2020 Frontiers in Bioscience. All rights reserved.

Background and Purpose: Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system. Methods: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. Results: After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%–10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization. © 2020 Elsevier Ltd