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Objective Methods Results Significance Focal cortical dysplasia (FCD) is a common cause of drug‐resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single‐center data. Our first objective is to compare them on independent multicenter test data. Additionally, we train and compare three new models and make them publicly available.We retrospectively collected FCD cases from four epilepsy centers. We chose three novel models that take two‐dimensional (2D) slices (2D‐nnUNet), 2.5D slices (FastSurferCNN), and large 3D patches (3D‐nnUNet) as inputs and trained them on a subset of Bonn data. As core evaluation metrics, we used voxel‐level Dice similarity coefficient (DSC), cluster‐level F1 score, subject‐level detection rate, and specificity.We collected 329 subjects, 244 diagnosed with FCD (27.7 ± 14.4 years old, 54% male) and 85 healthy controls (7.1 ± 2.4 years old, 51% female). We used 118 subjects for model training and kept the remaining subjects as an independent test set. 3D‐nnUNet achieved the highest F1 score of .58, the highest DSC of .36 (95% confidence interval [CI] = .30–.41), a detection rate of 55%, and a specificity of 86%. deepFCD showed the highest detection rate (82%) but had the lowest specificity (0%) and cluster‐level precision (.03, 95% CI = .03–.04, F1 score = .07). MELD showed the least performance variation across centers, with detection rates between 46% and 54%.This study shows the variance in performance for FCD detection models in a multicenter dataset. The two models with 3D input data showed the highest sensitivity. The 2D models performed worse than all other models, suggesting that FCD detection requires 3D data. The greatly improved precision of 3D‐nnUNet may make it a sensible choice to aid FCD detection. [ABSTRACT FROM AUTHOR]

Background/Objectives: Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. Methods: We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®. Results: The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®. Conclusions: The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance. [ABSTRACT FROM AUTHOR]

Purpose: The aim of our study was to assess the diagnostic performance of commercially available AI software for intracranial aneurysm detection and to determine if the AI system enhances the radiologist's accuracy in identifying aneurysms and reduces image analysis time. Methods: TOF-MRA clinical brain examinations were analyzed using commercially available software and by an consultant neuroradiologist for the presence of intracranial aneurysms. The results were compared with the reference standard, to measure the sensitivity and specificity of the software and the consultant neuroradiologist. Furthermore, we examined the time required for the neuroradiologist to analyze the TOF-MRA image set, both with and without use of the AI software. Results: In 500 TOF-MRI brain studies, 106 aneurysms were detected in 85 examinations by combining AI software with neuroradiologist readings. The neuroradiologist identified 98 aneurysms (92.5% sensitivity), while AI detected 77 aneurysms (72.6% sensitivity). Specificity and sensitivity were calculated from the combined effort as reference. Combining AI and neuroradiologist readings significantly improves detection reliability. Additionally, AI integration reduced TOF-MRA analysis time by 19 s (23% reduction). Conclusions: Our findings indicate that the AI-based software can support neuroradiologists in interpreting brain TOF-MRA. A combined reading of the AI-based software and the neuroradiologist demonstrated higher reliability in identifying intracranial aneurysms as compared to reading by either neuroradiologist or software, thus improving diagnostic accuracy of the neuroradiologist. Simultaneously, reading time for the neuroradiologist was reduced by approximately one quarter. [ABSTRACT FROM AUTHOR]

Background/Objectives: Computer-aided detection (CAD) systems for lung nodule detection often face challenges with 5 mm computed tomography (CT) scans, leading to missed nodules. This study assessed the efficacy of a deep learning-based slice thickness reduction technique from 5 mm to 1 mm to enhance CAD performance. Methods: In this retrospective study, 687 chest CT scans were analyzed, including 355 with nodules and 332 without nodules. CAD performance was evaluated on nodules, to which all three radiologists agreed. Results: The slice thickness reduction technique significantly improved the area under the receiver operating characteristic curve (AUC) for scan-level analysis from 0.867 to 0.902, with a p-value < 0.001, and nodule-level sensitivity from 0.826 to 0.916 at two false positives per scan. Notably, the performance showed greater improvements on smaller nodules than larger nodules. Qualitative analysis confirmed that nodules mistaken for ground glass on 5 mm scans could be correctly identified as part-solid on the refined 1 mm CT, thereby improving the diagnostic capability. Conclusions: Applying a deep learning-based slice thickness reduction technique significantly enhances CAD performance in lung nodule detection on chest CT scans, supporting the clinical adoption of refined 1 mm CT scans for more accurate diagnoses. [ABSTRACT FROM AUTHOR]

Background To improve tuberculosis case-finding, rapid, non-sputum triage tests need to be developed according to the World Health Organization target product profile (TPP) (>90% sensitivity, >70% specificity). We prospectively evaluated and compared artificial intelligence–based, computer-aided detection software, CAD4TBv7, and C-reactive protein assay (CRP) as triage tests at health facilities in Lesotho and South Africa. Methods Adults (≥18 years) presenting with ≥1 of the 4 cardinal tuberculosis symptoms were consecutively recruited between February 2021 and April 2022. After informed consent, each participant underwent a digital chest X-ray for CAD4TBv7 and a CRP test. Participants provided 1 sputum sample for Xpert MTB/RIF Ultra and Xpert MTB/RIF and 1 for liquid culture. Additionally, an expert radiologist read the chest X-rays via teleradiology. For primary analysis, a composite microbiological reference standard (ie, positive culture or Xpert Ultra) was used. Results We enrolled 1392 participants, 48% were people with HIV and 24% had previously tuberculosis. The receiver operating characteristic curve for CAD4TBv7 and CRP showed an area under the curve of.87 (95% CI:.84–.91) and.80 (95% CI:.76–.84), respectively. At thresholds corresponding to 90% sensitivity, specificity was 68.2% (95% CI: 65.4–71.0%) and 38.2% (95% CI: 35.3–41.1%) for CAD4TBv7 and CRP, respectively. CAD4TBv7 detected tuberculosis as well as an expert radiologist. CAD4TBv7 almost met the TPP criteria for tuberculosis triage. Conclusions CAD4TBv7 is accurate as a triage test for patients with tuberculosis symptoms from areas with a high tuberculosis and HIV burden. The role of CRP in tuberculosis triage requires further research. Clinical Trials Registration Clinicaltrials.gov identifier: NCT04666311. [ABSTRACT FROM AUTHOR]

The editorial discusses the effectiveness of artificial intelligence (AI) in improving colonoscopy quality for colorectal cancer (CRC) screening. AI, through computer-aided detection (CADe) and characterization (CADx), has shown promise in increasing adenoma detection rates (ADR) and polyp detection rates (PDR). However, there are limitations to AI implementation, such as the need for extensive training data and potential increased procedure time. While some studies have shown significant improvements in colonoscopy markers with AI, others have reported mixed results, indicating the need for further research and consideration of confounding factors before widespread adoption of AI in healthcare. [Extracted from the article]

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility. [ABSTRACT FROM AUTHOR]

The most commonly occurring cancer in the world is breast cancer with more than 500,000 cases across the world. The detection mechanism for breast cancer is endoscopist-dependent and necessitates a skilled pathologist. However, in recent years many computer-aided diagnoses (CADs) have been used to diagnose and classify breast cancer using traditional RGB images that analyze the images only in three-color channels. Nevertheless, hyperspectral imaging (HSI) is a pioneering non-destructive testing (NDT) image-processing technique that can overcome the disadvantages of traditional image processing which analyzes the images in a wide-spectrum band. Eight studies were selected for systematic diagnostic test accuracy (DTA) analysis based on the results of the Quadas-2 tool. Each of these studies' techniques is categorized according to the ethnicity of the data, the methodology employed, the wavelength that was used, the type of cancer diagnosed, and the year of publication. A Deeks' funnel chart, forest charts, and accuracy plots were created. The results were statistically insignificant, and there was no heterogeneity among these studies. The methods and wavelength bands that were used with HSI technology to detect breast cancer provided high sensitivity, specificity, and accuracy. The meta-analysis of eight studies on breast cancer diagnosis using HSI methods reported average sensitivity, specificity, and accuracy of 78%, 89%, and 87%, respectively. The highest sensitivity and accuracy were achieved with SVM (95%), while CNN methods were the most commonly used but had lower sensitivity (65.43%). Statistical analyses, including meta-regression and Deeks' funnel plots, showed no heterogeneity among the studies and highlighted the evolving performance of HSI techniques, especially after 2019. [ABSTRACT FROM AUTHOR]

The use of computer-aided detection (CADe) has increased the adenoma detection rates (ADRs) during colorectal cancer (CRC) screening/surveillance in randomized controlled trials (RCTs) but has not shown benefit in real-world implementation studies. We performed a single-center pragmatic RCT to evaluate the impact of real-time CADe on ADRs in colonoscopy performed by community gastroenterologists. We enrolled 1100 patients undergoing colonoscopy for CRC screening, surveillance, positive fecal-immunohistochemical tests, and diagnostic indications at one community-based center from September 2022 to March 2023. Patients were randomly assigned (1:1) to traditional colonoscopy or real-time CADe. Blinded pathologists analyzed histopathologic findings. The primary outcome was ADR (the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy (APC), sessile-serrated lesion detection rate, and non-neoplastic resection rate. The median age was 55.5 years (interquartile range, 50–62 years), 61% were female, 72.7% were of Hispanic ethnicity, and 9.1% had inadequate bowel preparation. The ADR for the CADe group was significantly higher than the traditional colonoscopy group (42.5% vs 34.4%; P =.005). The mean APC was significantly higher in the CADe group compared with the traditional colonoscopy group (0.89 ± 1.46 vs 0.60 ± 1.12; P <.001). The improvement in adenoma detection was driven by increased detection of <5 mm adenomas. CADe had a higher sessile-serrated lesion detection rate than traditional colonoscopy (4.7% vs 2.0%; P =.01). The improvement in ADR with CADe was significantly higher in the first half of the study (47.2% vs 33.7%; P =.002) compared with the second half (38.7% vs 34.9%; P =.33). In a single-center pragmatic RCT, real-time CADe modestly improved ADR and APC in average-detector community endoscopists. (ClinicalTrials.gov number, NCT05963724). [ABSTRACT FROM AUTHOR]

In the present research, we describe a computer-aided detection (CAD) method aimed at automatic fetal head circumference (HC) measurement in 2D ultrasonography pictures during all trimesters of pregnancy. The HC might be utilized toward determining gestational age and tracking fetal development. This automated approach is particularly valuable in low-resource settings where access to trained sonographers is limited. The CAD system is divided into two steps: to begin, Haar-like characteristics were extracted from ultrasound pictures in order to train a classifier using random forests to find the fetal skull. We identified the HC using dynamic programming, an elliptical fit, and a Hough transform. The computer-aided detection (CAD) program was well-trained on 999 pictures (HC18 challenge data source), and then verified on 335 photos from all trimesters in an independent test set. A skilled sonographer and an expert in medicine personally marked the test set. We used the crown-rump length (CRL) measurement to calculate the reference gestational age (GA). In the first, second, and third trimesters, the median difference between the standard GA and the GA calculated by the skilled sonographer stayed at 0.7 ± 2.7, 0.0 ± 4.5, and 2.0 ± 12.0 days, respectively. The regular duration variance between the baseline GA and the health investigator's GA remained 1.5 ± 3.0, 1.9 ± 5.0, and 4.0 ± 14 a couple of days. The mean variance between the standard GA and the CAD system's GA remained between 0.5 and 5.0, with an additional variation of 2.9 to 12.5 days. The outcomes reveal that the computer-aided detection (CAD) program outperforms an expert sonographer. When paired with the classifications reported in the literature, the provided system achieves results that are comparable or even better. We have assessed and scheduled this computerized approach for HC evaluation, which includes information from all trimesters of gestation. [ABSTRACT FROM AUTHOR]

Introduction: Esophagogastroduodenoscopy is the most important tool to detect gastric cancer (GC). In this study, we developed a computer-aided detection (CADe) system to detect GC with white light imaging (WLI) and linked color imaging (LCI) modes and aimed to compare the performance of CADe with that of endoscopists. Methods: The system was developed based on the deep learning framework from 9,021 images in 385 patients between 2017 and 2020. A total of 116 LCI and WLI videos from 110 patients between 2017 and 2023 were used to evaluate per-case sensitivity and per-frame specificity. Results: The per-case sensitivity and per-frame specificity of CADe with a confidence level of 0.5 in detecting GC were 78.6% and 93.4% for WLI and 94.0% and 93.3% for LCI, respectively (p < 0.001). The per-case sensitivities of nonexpert endoscopists for WLI and LCI were 45.8% and 80.4%, whereas those of expert endoscopists were 66.7% and 90.6%, respectively. Regarding detectability between CADe and endoscopists, the per-case sensitivities for WLI and LCI were 78.6% and 94.0% in CADe, respectively, which were significantly higher than those for LCI in experts (90.6%, p = 0.004) and those for WLI and LCI in nonexperts (45.8% and 80.4%, respectively, p < 0.001); however, no significant difference for WLI was observed between CADe and experts (p = 0.134). Conclusions: Our CADe system showed significantly better sensitivity in detecting GC when used in LCI compared with WLI mode. Moreover, the sensitivity of CADe using LCI is significantly higher than those of expert endoscopists using LCI to detect GC. [ABSTRACT FROM AUTHOR]

Objective: The aim is to detect impacted teeth in panoramic radiology by refining the pretrained MedSAM model. Study design: Impacted teeth are dental issues that can cause complications and are diagnosed via radiographs. We modified SAM model for individual tooth segmentation using 1016 X-ray images. The dataset was split into training, validation, and testing sets, with a ratio of 16:3:1. We enhanced the SAM model to automatically detect impacted teeth by focusing on the tooth's center for more accurate results. Results: With 200 epochs, batch size equals to 1, and a learning rate of 0.001, random images trained the model. Results on the test set showcased performance up to an accuracy of 86.73%, F1-score of 0.5350, and IoU of 0.3652 on SAM-related models. Conclusion: This study fine-tunes MedSAM for impacted tooth segmentation in X-ray images, aiding dental diagnoses. Further improvements on model accuracy and selection are essential for enhancing dental practitioners' diagnostic capabilities. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation. Aims: This study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population. Methods: This case–control study utilized propensity score matching (PSM) for age, sex, race, and colonoscopy indication to analyze a database of patients who underwent colonoscopy at a single tertiary referral center between 2017 and 2023. Patients were excluded if the procedure was incomplete or aborted owing to poor preparation. The patients were categorized based on the use of AI during colonoscopy. Data on patient demographics and colonoscopy performance metrics were collected. Univariate and multivariate logistic regression models were used to compare the groups. Results: After PSM patients with adequately prepped colonoscopies (n = 1466), the likelihood of detecting hyperplastic polyps (OR = 2.0, 95%CI 1.7–2.5, p < 0.001), adenomas (OR = 1.47, 95%CI 1.19–1.81, p < 0.001), and sessile serrated polyps (OR = 1.90, 95%CI 1.20–3.03, p = 0.007) significantly increased with the inclusion of CADe. In inadequately prepped patients (n = 160), CADe exhibited a more pronounced impact on the polyp detection rate (OR = 4.34, 95%CI 1.6–6.16, p = 0.049) and adenomas (OR = 2.9, 95%CI 2.20–8.57, p < 0.001), with a marginal increase in withdrawal and procedure times. Conclusion: This study highlights the significant improvement in detecting diminutive polyps (< 5 mm) and sessile polyps using CADe, although notably, this benefit was only seen in patients with adequate bowel preparation. In conclusion, the integration of AI in colonoscopy, driven by artificial intelligence, promises to significantly enhance lesion detection and diagnosis, revolutionize the procedure's effectiveness, and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Architectural distortion (AD) is one of the most common findings on mammograms, and it may represent not only cancer but also a lesion such as a radial scar that may have an associated cancer. AD accounts for 18–45% missed cancer, and the positive predictive value of AD is approximately 74.5%. Early detection of AD leads to early diagnosis and treatment of the cancer and improves the overall prognosis. However, detection of AD is a challenging task. In this work, we propose a new approach for detecting architectural distortion in mammography images by combining preprocessing methods and a novel structure fusion attention model. The proposed structure-focused weighted orientation preprocessing method is composed of the original image, the architecture enhancement map, and the weighted orientation map, highlighting suspicious AD locations. The proposed structure fusion attention model captures the information from different channels and outperforms other models in terms of false positives and top sensitivity, which refers to the maximum sensitivity that a model can achieve under the acceptance of the highest number of false positives, reaching 0.92 top sensitivity with only 0.6590 false positive per image. The findings suggest that the combination of preprocessing methods and a novel network architecture can lead to more accurate and reliable AD detection. Overall, the proposed approach offers a novel perspective on detecting ADs, and we believe that our method can be applied to clinical settings in the future, assisting radiologists in the early detection of ADs from mammography, ultimately leading to early treatment of breast cancer patients. [ABSTRACT FROM AUTHOR]

Many people are affected by lung cancer and it kills a lot of people worldwide. Often times, lung cancer does not have symptoms in the early stage. Abnormal growths called pulmonary nodules may be found in the lungs. Some of the pulmonary nodules may be cancerous. So, identifying pulmonary nodules will help detect lung cancer at an early stage. V-3DResNets based on variants of residual network is proposed to improve the generalization capability of the pulmonary nodule detection system. A U-shaped encoder-decoder is used to effectively extract the features. Residual networks help to reduce the vanishing gradient problem in deep neural networks. A Slice Grouped Non Local (SGNL) module studies the relation among entities across Computed Tomography (CT) slices. The content-aware mechanism of the squeeze and excitation block assigns different weights to the channels and improves the performance of the nodule detection system. The proposed work is analyzed using the PN9 dataset that includes 8,798 CT Scans with nine types of pulmonary nodules which attained a Competition Performance metric (CPM) of 65.51%. [ABSTRACT FROM AUTHOR]

Purpose: Intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography images is a problem of increasing clinical importance. Recently, a streak of methods have shown promising performance by using segmentation neural networks. However, these methods may be less relevant in a clinical settings where diagnostic decisions rely on detecting objects rather than their segmentation. Methods: We introduce a 3D single-stage object detection method tailored for small object detection such as aneurysms. Our anchor-free method incorporates fast data annotation, adapted data sampling and generation to address class imbalance problem, and spherical representations for improved detection. Results: A comprehensive evaluation was conducted, comparing our method with the state-of-the-art SCPM-Net, nnDetection and nnUNet baselines, using two datasets comprising 402 subjects. The evaluation used adapted object detection metrics. Our method exhibited comparable or superior performance, with an average precision of 78.96%, sensitivity of 86.78%, and 0.53 false positives per case. Conclusion: Our method significantly reduces the detection complexity compared to existing methods and highlights the advantages of object detection over segmentation-based approaches for aneurysm detection. It also holds potential for application to other small object detection problems. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AIbased detection assistant tools.

This article discusses studies and real-world experiences related to the clinical application of artificial intelligence-based computer-aided detection (AI-CAD) software (LuCAS-plus, Monitor Corporation) in detecting pulmonary nodules. During clinical trials for lung cancer screening, AI-CAD exhibited performance comparable to that of medical professionals in terms of sensitivity and specificity. Studies revealed that applying AI-CAD for diagnosing pulmonary metastases led to high detection rates. The use of a nodule matching algorithm in diagnosing pulmonary metastases significantly reduced false non-metastasis results. In clinical settings, implementing AI-CAD enhanced the efficiency of pulmonary nodule detection, saving time and effort during CT reading. Overall, AI-CAD is expected to offer substantial support for lung cancer screening and the interpretation of chest CT scans for malignant tumor surveillance.

Simple Summary: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers, and most patients present with advanced or irresectable disease due to late recognition. Radiological imaging modalities such as CT scans are key in providing information on the presence or absence of tumors. However, an assessment of pancreatic cancer requires specific radiological expertise, and small tumors are easily overlooked. Computer-aided detection (CAD) using artificial intelligence (AI) techniques is promising and may help in the early detection of pancreatic tumors. In this study, we developed a deep learning-based tumor detection framework that can detect pancreatic head cancer on CT scans with high accuracy when incorporating clinically relevant information. We demonstrate that a tumor detection framework utilizing CT scans and secondary signs of pancreatic tumors results in an increased detection accuracy for the detection of pancreatic head tumors. The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AIbased detection assistant tools. [ABSTRACT FROM AUTHOR]

This article discusses studies and real-world experiences related to the clinical application of artificial intelligence-based computer-aided detection (AI-CAD) software (LuCAS-plus, Monitor Corporation) in detecting pulmonary nodules. During clinical trials for lung cancer screening, AI-CAD exhibited performance comparable to that of medical professionals in terms of sensitivity and specificity. Studies revealed that applying AI-CAD for diagnosing pulmonary metastases led to high detection rates. The use of a nodule matching algorithm in diagnosing pulmonary metastases significantly reduced false non-metastasis results. In clinical settings, implementing AI-CAD enhanced the efficiency of pulmonary nodule detection, saving time and effort during CT reading. Overall, AI-CAD is expected to offer substantial support for lung cancer screening and the interpretation of chest CT scans for malignant tumor surveillance. [ABSTRACT FROM AUTHOR]

Lung cancer poses a significant threat, contributing significantly to cancer-related mortality. Computer-aided detection plays a pivotal role, particularly in the automated identification of pulmonary nodules, assisting radiologists in diagnosis. Despite the remarkable efficacy of deep convolutional neural networks in lesion identification, the detection of small nodules remains an enduring challenge. A conventional automated detection framework encompasses two critical stages: candidate detection and false positive reduction. This study introduces a novel approach named ReRointNet, focusing on meticulous lung nodule localization and detection through strategically placed sample points. To enhance nodule detection, we propose integrating PointNet anchors with RPN anchors. PointNet, operating on local key points, facilitates this integration. The synergy achieved by merging these anchors within our RePointNet framework enhances nodule detection rates and substantially improves localization accuracy. Post-detection, identified nodules undergo classification using the 3D Convolutional Neural Networks (CNN) method. Our contribution presents a novel paradigm for nodule detection in lung Computed Tomography (CT) images, with reduced computational costs and improved memory efficiency. The combined utilization of RePointNet and 3DCNN demonstrates proficiency in identifying nodules of various sizes, including small nodules. Our research underscores the superiority of lung nodule identification through the utilization of RePointNet based on point information, surpassing conventional networks. Rigorous evaluations of the LUNA16 dataset reveal our method's superior performance compared to state-of-the-art systems, achieving a notable sensitivity of 91.6 percent at a speed of 0.9 frames per second. These findings underscore the potential of our proposed approach in advancing precise lung nodule diagnosis, offering invaluable support to healthcare practitioners and radiologists engaged in diagnosing lung cancer patients. [ABSTRACT FROM AUTHOR]

BACKGROUND: Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images. OBJECTIVE: To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques. METHODS: This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models. RESULTS: Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images. CONCLUSION: This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images. [ABSTRACT FROM AUTHOR]

Background and Aim: Computer‐aided detection (CADe) systems can efficiently detect polyps during colonoscopy. However, false‐positive (FP) activation is a major limitation of CADe. We aimed to compare the rate and causes of FP using CADe before and after an update designed to reduce FP. Methods: We analyzed CADe‐assisted colonoscopy videos recorded between July 2022 and October 2022. The number and causes of FPs and excessive time spent by the endoscopist on FP (ET) were compared pre‐ and post‐update using 1:1 propensity score matching. Results: During the study period, 191 colonoscopy videos (94 and 97 in the pre‐ and post‐update groups, respectively) were recorded. Propensity score matching resulted in 146 videos (73 in each group). The mean number of FPs and median ET per colonoscopy were significantly lower in the post‐update group than those in the pre‐update group (4.2 ± 3.7 vs 18.1 ± 11.1; P < 0.001 and 0 vs 16 s; P < 0.001, respectively). Mucosal tags, bubbles, and folds had the strongest association with decreased FP post‐update (pre‐update vs post‐update: 4.3 ± 3.6 vs 0.4 ± 0.8, 0.32 ± 0.70 vs 0.04 ± 0.20, and 8.6 ± 6.7 vs 1.6 ± 1.7, respectively). There was no significant decrease in the true positive rate (post‐update vs pre‐update: 95.0% vs 99.2%; P = 0.09) or the adenoma detection rate (post‐update vs pre‐update: 52.1% vs 49.3%; P = 0.87). Conclusions: The updated CADe can reduce FP without impairing polyp detection. A reduction in FP may help relieve the burden on endoscopists. [ABSTRACT FROM AUTHOR]

BACKGROUND: Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate. OBJECTIVE: In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images. METHODS: We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected. RESULTS: Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73. CONCLUSION: The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer. [ABSTRACT FROM AUTHOR]

Incorrectly diagnosing plant diseases can lead to various undesirable outcomes. This includes the potential for the misuse of unsuitable herbicides, resulting in harm to both plants and the environment. Examining plant diseases visually is a complex and challenging procedure that demands considerable time and resources. Moreover, it necessitates keen observational skills from agronomists and plant pathologists. Precise identification of plant diseases is crucial to enhance crop yields, ultimately guaranteeing the quality and quantity of production. The latest progress in deep learning (DL) models has demonstrated encouraging outcomes in the identification and classification of plant diseases. In the context of this study, we introduce a novel hybrid deep learning architecture named "CTPlantNet". This architecture employs convolutional neural network (CNN) models and a vision transformer model to efficiently classify plant foliar diseases, contributing to the advancement of disease classification methods in the field of plant pathology research. This study utilizes two open-access datasets. The first one is the Plant Pathology 2020-FGVC-7 dataset, comprising a total of 3526 images depicting apple leaves and divided into four distinct classes: healthy, scab, rust, and multiple. The second dataset is Plant Pathology 2021-FGVC-8, containing 18,632 images classified into six categories: healthy, scab, rust, powdery mildew, frog eye spot, and complex. The proposed architecture demonstrated remarkable performance across both datasets, outperforming state-of-the-art models with an accuracy (ACC) of 98.28% for Plant Pathology 2020-FGVC-7 and 95.96% for Plant Pathology 2021-FGVC-8. [ABSTRACT FROM AUTHOR]

Abstract BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work’s experimental results achieve high values of accuracy when compared to other existing models.

Abstract Background The wide heterogeneity in the appearance of breast lesions and normal breast structures can confuse computerized detection algorithms. Our purpose was therefore to develop a Lesion Highlighter (LH) that can improve the performance of computer-aided detection algorithms for detecting breast cancer on screening mammograms. Methods We hypothesized that a Cycle-GAN based Lesion Remover (LR) could act as an LH, which can improve the performance of lesion detection algorithms. We used 10,310 screening mammograms from 4,832 women that included 4,942 recalled lesions (BI-RADS 0) and 5,368 normal results (BI-RADS 1). We divided the dataset into Train:Validate:Test folds with the ratios of 0.64:0.16:0.2. We segmented image patches (400 × 400 pixels) from either lesions marked by MQSA radiologists or normal tissue in mammograms. We trained a Cycle-GAN to develop two GANs, where each GAN transferred the style of one image to another. We refer to the GAN transferring the style of a lesion to normal breast tissue as the LR. We then highlighted the lesion by color-fusing the mammogram after applying the LR to its original. Using ResNet18, DenseNet201, EfficientNetV2, and Vision Transformer as backbone architectures, we trained three deep networks for each architecture, one trained on lesion highlighted mammograms (Highlighted), another trained on the original mammograms (Baseline), and Highlighted and Baseline combined (Combined). We conducted ROC analysis for the three versions of each deep network on the test set. Results The Combined version of all networks achieved AUCs ranging from 0.963 to 0.974 for identifying the image with a recalled lesion from a normal breast tissue image, which was statistically improved (p-value

Background/Objectives: Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. Methods: We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®. Results: The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®. Conclusions: The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance.

This paper aims to provide a structured analysis of the performance metrics used in computer-aided detection (CAD) systems, specifically focusing on lung nodule detection in computed tomography (CT) images. By examining key metrics along with their respective strengths and limitations, this study offers guidelines to assist in selecting appropriate metrics. Evaluation methods for CAD systems for lung nodule detection are primarily categorized into per-scan and per-nodule approaches. For per-scan analysis, a key metric is the area under the receiver operating characteristic (ROC) curve (AUROC), which evaluates the ability of the system to distinguish between scans with and without nodules. For per-nodule analysis, the nodule-level sensitivity at fixed false positives per scan is often used, supplemented by the free-response receiver operating characteristic (FROC) curve and the competition performance metric (CPM). However, the CPM does not provide normalized scores because it theoretically ranges from zero to infinity and largely varies depending on the characteristics of the data. To address the advantages and limitations of ROC and FROC curves, an alternative FROC (AFROC) was introduced to combine the strengths of both per-scan and per-nodule analyses. This paper discusses the principles of each metric and their relative strengths, providing insights into their clinical implications and practical utility.

Background/Objectives: Computer-aided detection (CAD) systems for lung nodule detection often face challenges with 5 mm computed tomography (CT) scans, leading to missed nodules. This study assessed the efficacy of a deep learning-based slice thickness reduction technique from 5 mm to 1 mm to enhance CAD performance. Methods: In this retrospective study, 687 chest CT scans were analyzed, including 355 with nodules and 332 without nodules. CAD performance was evaluated on nodules, to which all three radiologists agreed. Results: The slice thickness reduction technique significantly improved the area under the receiver operating characteristic curve (AUC) for scan-level analysis from 0.867 to 0.902, with a p-value < 0.001, and nodule-level sensitivity from 0.826 to 0.916 at two false positives per scan. Notably, the performance showed greater improvements on smaller nodules than larger nodules. Qualitative analysis confirmed that nodules mistaken for ground glass on 5 mm scans could be correctly identified as part-solid on the refined 1 mm CT, thereby improving the diagnostic capability. Conclusions: Applying a deep learning-based slice thickness reduction technique significantly enhances CAD performance in lung nodule detection on chest CT scans, supporting the clinical adoption of refined 1 mm CT scans for more accurate diagnoses.

Purpose: During stereotactic radiosurgery (SRS) planning for brain metastases (BM), brain MRIs are reviewed to select appropriate targets based on radiographic characteristics. Some BM are difficult to detect and/or definitively identify and may go untreated initially, only to become apparent on future imaging. We hypothesized that in patients receiving multiple courses of SRS, reviewing the initial planning MRI would reveal early evidence of lesions that developed into metastases requiring SRS. Methods: Patients undergoing two or more courses of SRS to BM within 6 months between 2016 and 2018 were included in this single-institution, retrospective study. Brain MRIs from the initial course were reviewed for lesions at the same location as subsequently treated metastases; if present, this lesion was classified as a "retrospectively identified metastasis" or RIM. RIMs were subcategorized as meeting or not meeting diagnostic imaging criteria for BM (+ DC or -DC, respectively). Results: Among 683 patients undergoing 923 SRS courses, 98 patients met inclusion criteria. There were 115 repeat courses of SRS, with 345 treated metastases in the subsequent course, 128 of which were associated with RIMs found in a prior MRI. 58% of RIMs were + DC. 17 (15%) of subsequent courses consisted solely of metastases associated with + DC RIMs. Conclusion: Radiographic evidence of brain metastases requiring future treatment was occasionally present on brain MRIs from prior SRS treatments. Most RIMs were + DC, and some subsequent SRS courses treated only + DC RIMs. These findings suggest enhanced BM detection might enable earlier treatment and reduce the need for additional SRS. [ABSTRACT FROM AUTHOR]

Background Computer-aided detection (CAD) may be a useful screening tool for tuberculosis (TB). However, there are limited data about its utility in active case finding (ACF) in a community-based setting, and particularly in an HIV-endemic setting where performance may be compromised. Methods We performed a systematic review and evaluated articles published between January 2012 and February 2023 that included CAD as a screening tool to detect pulmonary TB against a microbiological reference standard (sputum culture and/or nucleic acid amplification test [NAAT]). We collected and summarized data on study characteristics and diagnostic accuracy measures. Two reviewers independently extracted data and assessed methodological quality against Quality Assessment of Diagnostic Accuracy Studies–2 criteria. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines were followed. Results Of 1748 articles reviewed, 5 met with the eligibility criteria and were included in this review. A meta-analysis revealed pooled sensitivity of 0.87 (95% CI, 0.78–0.96) and specificity of 0.74 (95% CI, 0.55–0.93), just below the World Health Organization (WHO)–recommended target product profile (TPP) for a screening test (sensitivity ≥0.90 and specificity ≥0.70). We found a high risk of bias and applicability concerns across all studies. Subgroup analyses, including the impact of HIV and previous TB, were not possible due to the nature of the reporting within the included studies. Conclusions This review provides evidence, specifically in the context of ACF, for CAD as a potentially useful and cost-effective screening tool for TB in a resource-poor HIV-endemic African setting. However, given methodological concerns, caution is required with regards to applicability and generalizability. [ABSTRACT FROM AUTHOR]

Objectives: A computer‐aided detection (CAD) system was developed to support the detection of colorectal lesions by deep learning using video images of lesions and normal mucosa recorded during colonoscopy. The study's purpose was to evaluate the stand‐alone performance of this device under blinded conditions. Methods: This multicenter prospective observational study was conducted at four Japanese institutions. We used 326 videos of colonoscopies recorded with patient consent at institutions in which the Ethics Committees approved the study. The sensitivity of successful detection of the CAD system was calculated using the target lesions, which were detected by adjudicators from two facilities for each lesion appearance frame; inconsistencies were settled by consensus. Successful detection was defined as display of the detection flag on the lesion for more than 0.5 s within 3 s of appearance. Results: Of the 556 target lesions from 185 cases, detection success sensitivity was 97.5% (95% confidence interval [CI] 95.8–98.5%). The "successful detection sensitivity per colonoscopy" was 93% (95% CI 88.3–95.8%). For the frame‐based sensitivity, specificity, positive predictive value, and negative predictive value were 86.6% (95% CI 84.8–88.4%), 84.7% (95% CI 83.8–85.6%), 34.9% (95% CI 32.3–37.4%), and 98.2% (95% CI 97.8–98.5%), respectively. Trial registration: University Hospital Medical Information Network (UMIN000044622). [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS). ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared. For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832–0.908). The AUC significantly improved to 0.919 (95% CI, 0.890–0.947; P = 0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844–0.923), 0.890 (95% CI, 0.852–0.929), and 0.890 (95% CI, 0.853–0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon, and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing. AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool. [ABSTRACT FROM AUTHOR]

Summary: Background: Contrast-enhanced CT scans provide a means to detect unsuspected colorectal cancer. However, colorectal cancers in contrast-enhanced CT without bowel preparation may elude detection by radiologists. We aimed to develop a deep learning (DL) model for accurate detection of colorectal cancer, and evaluate whether it could improve the detection performance of radiologists. Methods: We developed a DL model using a manually annotated dataset (1196 cancer vs 1034 normal). The DL model was tested using an internal test set (98 vs 115), two external test sets (202 vs 265 in 1, and 252 vs 481 in 2), and a real-world test set (53 vs 1524). We compared the detection performance of the DL model with radiologists, and evaluated its capacity to enhance radiologists’ detection performance. Findings: In the four test sets, the DL model had the area under the receiver operating characteristic curves (AUCs) ranging between 0.957 and 0.994. In both the internal test set and external test set 1, the DL model yielded higher accuracy than that of radiologists (97.2% vs 86.0%, p 0.99), and it detected 2 cases that had been missed by radiologists. Interpretation: The developed DL model can accurately detect colorectal cancer and improve radiologists’ detection performance, showing its potential as an effective computer-aided detection tool. Funding: This study was supported by National Science Fund for Distinguished Young Scholars of China (No. 81925023); Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20345); National Natural Science Foundation of China (No. 82072090 and No. 82371954); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No. 2022B1212010011); High-level Hospital Construction Project (No. DFJHBF202105).

Abstract Background Artificial intelligence-based computer-aided detection (AI–CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI–CAD for pulmonary tuberculosis (TB) in clinical settings. However, little is known about its applicability to community-based active case-finding (ACF) for TB. Methods We analysed an anonymized data set obtained from a community-based ACF in Cambodia, targeting persons aged 55 years or over, persons with any TB symptoms, such as chronic cough, and persons at risk of TB, including household contacts. All of the participants in the ACF were screened by chest radiography (CXR) by Cambodian doctors, followed by Xpert test when they were eligible for sputum examination. Interpretation by an experienced chest physician and abnormality scoring by a newly developed AI–CAD were retrospectively conducted for the CXR images. With a reference of Xpert-positive TB or human interpretations, receiver operating characteristic (ROC) curves were drawn to evaluate the AI–CAD performance by area under the ROC curve (AUROC). In addition, its applicability to community-based ACFs in Cambodia was examined. Results TB scores of the AI–CAD were significantly associated with the CXR classifications as indicated by the severity of TB disease, and its AUROC as the bacteriological reference was 0.86 (95% confidence interval 0.83–0.89). Using a threshold for triage purposes, the human reading and bacteriological examination needed fell to 21% and 15%, respectively, detecting 95% of Xpert-positive TB in ACF. For screening purposes, we could detect 98% of Xpert-positive TB cases. Conclusions AI–CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce. The use of AI–CAD in developing countries has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour. Further studies are needed to generalize the results to other countries by increasing the sample size and comparing the AI–CAD performance with that of more human readers.

Aneurysms pose a life-threatening risk due to weakened vessel walls, causing bulging or ballooning in arterial blood vessels. The growth of an aneurysm increases the risk of rupture and consequent bleeding in the brain, leading to a hemorrhagic stroke. Therefore, accurate detection and segmentation of intracranial aneurysms are crucial for treatment planning in patients. Recently, the use of Time of Flight Magnetic Resonance Angiography (TOF-MRA) for automated segmentation of intracranial aneurysms has gained significant importance. This study comprehensively evaluates different automated segmentation methods for unruptured intracranial aneurysms, using the publicly available Aneurysm Detection and Segmentation (ADAM) challenge dataset. The performance and method scalability of these methods is analyzed across state-of-the-art algorithms, and the experimental analysis shows that 3D U-Net architecture outperforms in the segmentation tasks.

Ultrasound (US) is a widely accessible and extensively used tool for breast imaging. It is commonly used as an additional screening tool, especially for women with dense breast tissue. Advances in artificial intelligence (AI) have led to the development of various AI systems that assist radiologists in identifying and diagnosing breast lesions using US. This article provides an overview of the background and supporting evidence for the use of AI in hand held breast US. It discusses the impact of AI on clinical workflow, covering breast cancer detection, diagnosis, prediction of molecular subtypes, evaluation of axillary lymph node status, and response to neoadjuvant chemotherapy. Additionally, the article highlights the potential significance of AI in breast US for low and middle income countries.