Machine learning applications for vascular stenosis detection in computed tomography angiography: a systematic review and meta-analysis
Abstract
In an era in which cardiovascular disease has become the main cause of death all over the world, diagnostic accuracy in identifying blood vessels has become particularly important. Vascular stenosis causes serious health risks by affecting blood flow, leading to conditions like heart attacks and strokes. Traditional diagnostic methods face challenges in terms of timeliness and accuracy. Our systematic review aims to critically assess the role of machine learning (ML) techniques in enhancing computed tomography angiography’s (CTA) diagnostic capabilities for vasoconstriction. This review followed the predetermined inclusion and exclusion criteria and focused on research articles published between January 2013 and October 2023 collected from databases such as PubMed, IEEE, Web of Science, and Scopus. Studies focus on multiphase CTA or dynamic CTA; papers do not use the ML; and papers not in English are removed. The risk of bias of included studies was evaluated using the QUADAS2 tool. The results were analyzed in tabular form using metrics such as accuracy, sensitivity, and specificity and examine variations in stenosis detection by anatomical regions. In our review, a total of 63 studies were identified as relevant. These studies included a variety of ML applications for identifying anatomical stenosis of the arteries in different anatomical areas. The findings highlighted a trend of high sensitivity and specificity in broader anatomical assessments, with nuanced variations…
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Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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