In this review, we focused on the applicability of artificial intelligence (AI) for opportunistic abdominal aortic aneurysm (AAA) detection in computed tomography (CT). We used the academic search system PubMed as the primary source for the literature search and Google Scholar as a supplementary source of evidence. We searched through 2 February 2022. All studies on automated AAA detection or segmentation in noncontrast abdominal CT were included. For bias assessment, we developed and used an adapted version of the QUADAS-2 checklist. We included eight studies with 355 cases, of which 273 (77%) contained AAA. The highest risk of bias and level of applicability concerns were observed for the “patient selection” domain, due to the 100% pathology rate in the majority (75%) of the studies. The mean sensitivity value was 95% (95% CI 100–87%), the mean specificity value was 96.6% (95% CI 100–75.7%), and the mean accuracy value was 95.2% (95% CI 100–54.5%). Half of the included studies performed diagnostic accuracy estimation, with only one study having data on all diagnostic accuracy metrics. Therefore, we conducted a narrative synthesis. Our findings indicate high study heterogeneity, requiring further research with balanced noncontrast CT datasets and adherence to reporting standards in order to validate the high sensitivity value obtained.

BACKGROUND: The global amount of investment in companies developing artificial intelligence (AI)-based software technologies for medical diagnostics reached $80 million in 2016, rose to $152 million in 2017, and is expected to continue growing. While software manufacturing companies should comply with existing clinical, bioethical, legal, and methodological frameworks and standards, there is a lack of uniform national and international standards and protocols for testing and monitoring AI-based software. AIM: This objective of this study is to develop a universal methodology for testing and monitoring AI-based software for medical diagnostics, with the aim of improving its quality and implementing its integration into practical healthcare. MATERIALS AND METHODS: The research process involved an analytical phase in which a literature review was conducted on the PubMed and eLibrary databases. The practical stage included the approbation of the developed methodology within the framework of an experiment focused on the use of innovative technologies in the field of computer vision to analyze medical images and further application in the health care system of the city of Moscow. RESULTS: A methodology for testing and monitoring AI-based software for medical diagnostics has been developed, aimed at improving its quality and introducing it into practical healthcare. The methodology consists of seven stages: self-testing, functional testing, calibration testing, technological monitoring, clinical monitoring, feedback, and refinement. CONCLUSION: Distinctive features of the methodology include its cyclical stages of monitoring and software development, leading to continuous improvement of its quality, the presence of detailed requirements for the results of the software work, and the participation of doctors in software evaluation. The methodology will allow software developers to achieve significant outcomes and demonstrate achievements across various areas. It also empowers users to make informed and confident choices among software options that have passed an independent and comprehensive quality check.

Rationale and Objectives: Post-COVID condition (PCC) is associated with long-term neuropsychiatric symptoms. Magnetic resonance imaging (MRI) in PCC examines the brain metabolism, connectivity, and morphometry. Such techniques are not easily available in routine practice. We conducted a scoping review to determine what is known about the routine MRI findings in PCC patients. Materials and Methods: The PubMed database was searched up to 11 April 2023. We included cohort, cross-sectional, and before–after studies in English. Articles with only advanced MRI sequences (DTI, fMRI, VBM, PWI, ASL), preprints, and case reports were excluded. The National Heart, Lung, and Blood Institute and PRISMA Extension tools were used for quality assurance. Results: A total of 7 citations out of 167 were included. The total sample size was 451 patients (average age 51 ± 8 years; 67% female). Five studies followed a single recovering cohort, while two studies compared findings between two severity groups. The MRI findings were perivascular spaces (47%), microbleeds (27%) and white matter lesions (10%). All the studies agreed that PCC manifestations are not associated with specific MRI findings. Conclusion: The results of the included studies are heterogeneous due to the low agreement on the types of MRI abnormalities in PCC. Our findings indicate that the routine brain MRI protocol has little value for long COVID diagnostics.

We performed a multicenter external evaluation of the practical and clinical efficacy of a commercial AI algorithm for chest X-ray (CXR) analysis (Lunit INSIGHT CXR). A retrospective evaluation was performed with a multi-reader study. For a prospective evaluation, the AI model was run on CXR studies; the results were compared to the reports of 226 radiologists. In the multi-reader study, the area under the curve (AUC), sensitivity, and specificity of the AI were 0.94 (CI95%: 0.87–1.0), 0.9 (CI95%: 0.79–1.0), and 0.89 (CI95%: 0.79–0.98); the AUC, sensitivity, and specificity of the radiologists were 0.97 (CI95%: 0.94–1.0), 0.9 (CI95%: 0.79–1.0), and 0.95 (CI95%: 0.89–1.0). In most regions of the ROC curve, the AI performed a little worse or at the same level as an average human reader. The McNemar test showed no statistically significant differences between AI and radiologists. In the prospective study with 4752 cases, the AUC, sensitivity, and specificity of the AI were 0.84 (CI95%: 0.82–0.86), 0.77 (CI95%: 0.73–0.80), and 0.81 (CI95%: 0.80–0.82). Lower accuracy values obtained during the prospective validation were mainly associated with false-positive findings considered by experts to be clinically insignificant and the false-negative omission of human-reported “opacity”, “nodule”, and calcification. In a large-scale prospective validation of the commercial AI algorithm in clinical practice, lower sensitivity and specificity values were obtained compared to the prior retrospective evaluation of the data of the same population.

BACKGROUND: In recent years, the availability of medical datasets and technologies for software development based on artificial intelligence technology has resulted in a growth in the number of solutions for medical diagnostics, particularly mammography. Registered as a medical device, this program can interpret digital mammography, significantly saving time, material, and human resources in healthcare while ensuring the quality of mammary gland preventive studies. AIM: This study aims to justify the possibility and effectiveness of artificial intelligence-based software for the first interpretation of digital mammograms while maintaining the practice of a radiologists second description of X-ray images. MATERIALS AND METHODS: A dataset of 100 digital mammography studies (50 absence of target pathology and 50 ― presence of target pathology, with signs of malignant neoplasms) was processed by software based on artificial intelligence technology that was registered as a medical device in the Russian Federation. Receiver operating characteristic analysis was performed. Limitations of the study include the values of diagnostic accuracy metrics obtained for software based on artificial intelligence technology versions, relevant at the end of 2022. RESULTS: When set to 80.0% sensitivity, artificial intelligence specificity was 90.0% (95% CI, 81.798.3), and accuracy was 85.0% (95% CI, 78.092.0). When set to 100% specificity, artificial intelligence demonstrated 56.0% sensitivity (95% CI, 42.269.8) and 78.0% accuracy (95% CI, 69.986.1). When the sensitivity was set to 100%, the artificial intelligence specificity was 54.0% (95% CI, 40.267.8), and the accuracy was 77.0% (95% CI, 68.885.2). Two approaches have been proposed, providing an autonomous first interpretation of digital mammography using artificial intelligence. The first approach is to evaluate the X-ray image using artificial intelligence with a higher sensitivity than that of the double-reading mammogram by radiologists, with a comparable level of specificity. The second approach implies that artificial intelligence-based software will determine the mammogram category (absence of target pathology or presence of target pathology), indicating the degree of confidence in the obtained result, depending on the corridor into which the predicted value falls. CONCLUSIONS: Both proposed approaches for using artificial intelligence-based software for the autonomous first interpretation of digital mammograms can provide diagnostic quality comparable to, if not superior to, double-image reading by radiologists. The economic benefit from the practical implementation of this approach nationwide can range from 0.6 to 5.5 billion rubles annually.

Background: The integration of artificial intelligence (AI) into medicine is growing, with some experts predicting its standalone use soon. However, skepticism remains due to limited positive outcomes from independent validations. This research evaluates AI software’s effectiveness in analyzing chest X-rays (CXR) to identify lung nodules, a possible lung cancer indicator. Methods: This retrospective study analyzed 7,670,212 record pairs from radiological exams conducted between 2020 and 2022 during the Moscow Computer Vision Experiment, focusing on CXR and computed tomography (CT) scans. All images were acquired during clinical routine. The final dataset comprised 100 CXR images (50 with lung nodules, 50 without), selected consecutively and based on inclusion and exclusion criteria, to evaluate the performance of all five AI-based solutions, participating in the Moscow Computer Vision Experiment and analyzing CXR. The evaluation was performed in 3 stages. In the first stage, the probability of a nodule in the lung obtained from AI services was compared with the Ground Truth (1—there is a nodule, 0—there is no nodule). In the second stage, 3 radiologists evaluated the segmentation of nodules performed by the AI services (1—nodule correctly segmented, 0—nodule incorrectly segmented or not segmented at all). In the third stage, the same radiologists additionally evaluated the classification of the nodules (1—nodule correctly segmented and classified, 0—all other cases). The results obtained in stages 2 and 3 were compared with Ground Truth, which was common to all three stages. For each stage, diagnostic accuracy metrics were calculated for each AI service. Results: Three software solutions (Celsus, Lunit INSIGHT CXR, and qXR) demonstrated diagnostic metrics that matched or surpassed the vendor specifications, and achieved the highest area under the receiver operating characteristic curve (AUC) of 0.956 [95% confidence interval (CI): 0.918 to 0.994]. However, when evaluated by three radiologists for accurate nodule segmentation and classification, all solutions performed below the vendor-declared metrics, with the highest AUC reaching 0.812 (95% CI: 0.744 to 0.879). Meanwhile, all AI services demonstrated 100% specificity at stages 2 and 3 of the study. Conclusions: To ensure the reliability and applicability of AI-based software, it is crucial to validate performance metrics using high-quality datasets and engage radiologists in the evaluation process. Developers are recommended to improve the accuracy of the underlying models before allowing the standalone use of the software for lung nodule detection. The dataset created during the study may be accessed at https://mosmed.ai/datasets/mosmeddatargogksnalichiemiotsutstviemlegochnihuzlovtipvii/.