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SYSTEMATIC REVIEW article

Front. Cardiovasc. Med.
Sec. Cardiovascular Epidemiology and Prevention
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1398963

Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaques: a systematic review and metaanalysis

Provisionally accepted
Jie Pingping Jie Pingping 1*Fan Min Fan Min 1*Zhang Haiyi Zhang Haiyi 1Wang Oucheng Wang Oucheng 1*Lv Jun Lv Jun 1*Liu Yingchun Liu Yingchun 1*Chunyin Zhang Chunyin Zhang 2Yong Liu Yong Liu 1*Jie Zhao Jie Zhao 1,3*
  • 1 Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
  • 2 The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
  • 3 Southwest Medical University, Luzhou, China

The final, formatted version of the article will be published soon.

    Abstract Background Artificial intelligence (AI) has increasingly been applied to computed tomography angiography (CTA) images to aid in the assessment of atherosclerotic plaques. Our aim was to explore the diagnostic accuracy of AI-assisted CTA for plaque diagnosis and classification through a systematic review and meta-analysis. Methods A systematic literature review was performed by searching PubMed, EMBASE, and the Cochrane Library according to PRISMA guidelines. Original studies evaluating the diagnostic accuracy of radiomics, machine learning, or deep learning techniques applied to CTA images for detecting stenosis, calcification, or plaque vulnerability were included. The quality and risk of bias of the included studies were evaluated using the QUADAS-2 tool. The meta-analysis was conducted using STATA software (version 17.0) to pool sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) to determine the overall diagnostic performance. Results Eleven studies comprising 1484 patients were included. There was low risk of bias and substantial heterogeneity. The overall pooled AUROC for atherosclerotic plaque assessment was 0.96 (95% CI 0.94-0.97) across 21 trials. Of which for ≥ 50% stenosis detection, the AUROC was 0.95 (95% CI 0.93-0.96) in 5 studies. For identifying ≥ 70% stenosis, the AUROC was 0.96 (95% CI 0.94-0.97) in 6 studies. For calcium detection, the AUROC was 0.92 (95% CI 0.90-0.94) in 6 studies. Conclusion Our meta-analysis demonstrates that AI-assisted CTA has high diagnostic accuracy for detecting stenosis and characterizing plaque composition, with optimal performance in detecting ≥ 70% stenosis.

    Keywords: AI, CTA, plaques, assessment, Meta-analysis, Systematic review

    Received: 12 Mar 2024; Accepted: 20 Aug 2024.

    Copyright: © 2024 Pingping, Min, Haiyi, Oucheng, Jun, Yingchun, Zhang, Liu and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jie Pingping, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
    Fan Min, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
    Wang Oucheng, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
    Lv Jun, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
    Liu Yingchun, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
    Yong Liu, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China
    Jie Zhao, Southwest Medical University, Luzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.