Pingping Jie, Min Fan, Haiyi Zhang, Oucheng Wang, Jun Lv, Yingchun Liu, Chunyin Zhang, Yong Liu, Jie Zhao
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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.ResultsA total of 11 studies comprising 1,484 patients were included. There was low risk of bias and substantial heterogeneity. The overall pooled AUROC for atherosclerotic plaque assessment was 0.96 [95% confidence interval (CI) 0.94–0.97] across 21 trials. Of these, for ≥50% stenosis detection, the AUROC was 0.95 (95% CI 0.93–0.96) in five studies. For identifying ≥70% stenosis, the AUROC was 0.96 (95% CI 0.94–0.97) in six studies. For calcium detection, the AUROC was 0.92 (95% CI 0.90–0.94) in six studies.ConclusionOur 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.Systematic Review Registration<jats:uri>https://www.crd.york.ac.uk/</jats:uri>, PROSPERO, identifier (CRD42023431410).","PeriodicalId":12414,"journal":{"name":"Frontiers in Cardiovascular Medicine","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis\",\"authors\":\"Pingping Jie, Min Fan, Haiyi Zhang, Oucheng Wang, Jun Lv, Yingchun Liu, Chunyin Zhang, Yong Liu, Jie Zhao\",\"doi\":\"10.3389/fcvm.2024.1398963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BackgroundArtificial intelligence (AI) has increasingly been applied to computed tomography angiography (CTA) images to aid in the assessment of atherosclerotic plaque. 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引用次数: 0
摘要
背景人工智能(AI)越来越多地应用于计算机断层扫描血管造影(CTA)图像,以帮助评估动脉粥样硬化斑块。我们的目的是通过系统综述和荟萃分析,探讨人工智能辅助 CTA 对斑块诊断和分类的诊断准确性。方法根据 PRISMA 指南检索 PubMed、EMBASE 和 Cochrane 图书馆,进行系统文献综述。纳入的原始研究评估了应用于 CTA 图像的放射组学、机器学习或深度学习技术在检测狭窄、钙化或斑块脆弱性方面的诊断准确性。使用 QUADAS-2 工具对纳入研究的质量和偏倚风险进行了评估。使用 STATA 软件(17.0 版)进行荟萃分析,汇总灵敏度、特异性和接收者操作特征曲线下面积(AUROC),以确定总体诊断性能。偏倚风险较低,异质性较大。在21项试验中,动脉粥样硬化斑块评估的总合AUROC为0.96[95%置信区间(CI)0.94-0.97]。其中,对于≥50%狭窄的检测,5项研究的AUROC为0.95(95% CI 0.93-0.96)。在识别≥70%狭窄方面,6项研究的AUROC为0.96(95% CI 0.94-0.97)。结论我们的荟萃分析表明,AI辅助CTA在检测狭窄和描述斑块组成方面具有很高的诊断准确性,在检测≥70%的狭窄方面具有最佳性能。系统综述注册https://www.crd.york.ac.uk/,PROSPERO,标识符(CRD42023431410)。
Diagnostic value of artificial intelligence-assisted CTA for the assessment of atherosclerosis plaque: a systematic review and meta-analysis
BackgroundArtificial intelligence (AI) has increasingly been applied to computed tomography angiography (CTA) images to aid in the assessment of atherosclerotic plaque. Our aim was to explore the diagnostic accuracy of AI-assisted CTA for plaque diagnosis and classification through a systematic review and meta-analysis.MethodsA 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.ResultsA total of 11 studies comprising 1,484 patients were included. There was low risk of bias and substantial heterogeneity. The overall pooled AUROC for atherosclerotic plaque assessment was 0.96 [95% confidence interval (CI) 0.94–0.97] across 21 trials. Of these, for ≥50% stenosis detection, the AUROC was 0.95 (95% CI 0.93–0.96) in five studies. For identifying ≥70% stenosis, the AUROC was 0.96 (95% CI 0.94–0.97) in six studies. For calcium detection, the AUROC was 0.92 (95% CI 0.90–0.94) in six studies.ConclusionOur 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.Systematic Review Registrationhttps://www.crd.york.ac.uk/, PROSPERO, identifier (CRD42023431410).
期刊介绍:
Frontiers? Which frontiers? Where exactly are the frontiers of cardiovascular medicine? And who should be defining these frontiers?
At Frontiers in Cardiovascular Medicine we believe it is worth being curious to foresee and explore beyond the current frontiers. In other words, we would like, through the articles published by our community journal Frontiers in Cardiovascular Medicine, to anticipate the future of cardiovascular medicine, and thus better prevent cardiovascular disorders and improve therapeutic options and outcomes of our patients.