Performance of artificial intelligence in detecting the chronic total occlusive lesions of coronary artery based on coronary computed tomographic angiography.

IF 2.1 3区医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS

Cardiovascular diagnosis and therapy Pub Date : 2024-08-31 Epub Date: 2024-06-20 DOI:10.21037/cdt-23-407

Yanying Yang, Zhen Zhou, Nan Zhang, Rui Wang, Yifeng Gao, Xiaowei Ran, Zhonghua Sun, Heye Zhang, Guang Yang, Xiantao Song, Lei Xu

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引用次数: 0

Abstract

Background: Coronary chronic total occlusion (CTO) increases the risk of developing major adverse cardiovascular events (MACE) and cardiogenic shock. Coronary computed tomography angiography (CCTA) is a safe, noninvasive method to diagnose CTO lesions. With the development of artificial intelligence (AI), AI has been broadly applied in cardiovascular images, but AI-based detection of CTO lesions from CCTA images is difficult. We aim to evaluate the performance of AI in detecting the CTO lesions of coronary arteries based on CCTA images.

Methods: We retrospectively and consecutively enrolled patients with 50% stenosis, 50-99% stenosis, and CTO lesions who received CCTA scans between June 2021 and June 2022 in Beijing Anzhen Hospital. Four-fifths of them were randomly assigned to the training dataset, while the rest (1/5) were randomly assigned to the testing dataset. Performance of the AI-assisted CCTA (CCTA-AI) in detecting the CTO lesions was evaluated through sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis. With invasive coronary angiography as the reference, the diagnostic performance of AI method and manual method was compared.

Results: A total of 537 patients with 1,569 stenotic lesions (including 672 lesions with <50% stenosis, 493 lesions with 50-99% stenosis, and 404 CTO lesions) were enrolled in our study. CCTA-AI saved 75% of the time in post-processing and interpreting the CCTA images when compared to the manual method (116±15 vs. 472±45 seconds). In the testing dataset, the accuracy of CCTA-AI in detecting CTO lesions was 86.2% (79.0%, 90.3%), with the area under the curve of 0.874. No significant difference was found in detecting CTO lesions between AI and manual methods (P=0.53).

Conclusions: AI can automatically detect CTO lesions based on CCTA images, with high diagnostic accuracy and efficiency.

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基于冠状动脉计算机断层扫描血管造影的人工智能在检测冠状动脉慢性全闭塞病变中的表现。

背景：冠状动脉慢性全闭塞（CTO）会增加发生重大不良心血管事件（MACE）和心源性休克的风险。冠状动脉计算机断层扫描（CCTA）是诊断 CTO 病变的一种安全、无创的方法。随着人工智能（AI）的发展，AI 已广泛应用于心血管图像，但基于 AI 的 CCTA 图像 CTO 病变检测却很困难。我们旨在评估人工智能基于CCTA图像检测冠状动脉CTO病变的性能：方法：我们回顾性地连续纳入了 2021 年 6 月至 2022 年 6 月期间在北京安贞医院接受 CCTA 扫描的 50%、50%-99% 和 CTO 病变患者。其中五分之四随机分配到训练数据集，其余五分之一随机分配到测试数据集。通过敏感性、特异性、阳性预测值、阴性预测值、准确性和接收者操作特征分析，评估人工智能辅助 CCTA（CCTA-AI）检测 CTO 病变的性能。以有创冠状动脉造影为参照，比较了人工智能方法和手动方法的诊断性能：共有 537 名患者，1,569 个狭窄病变（包括 672 个病变，vs.472±45 秒）。在测试数据集中，CCTA-AI 检测 CTO 病变的准确率为 86.2%（79.0%，90.3%），曲线下面积为 0.874。人工智能与手动方法在检测CTO病变方面无明显差异（P=0.53）：结论：人工智能可根据 CCTA 图像自动检测 CTO 病变，诊断准确率高，效率高。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cardiovascular diagnosis and therapy Medicine-Cardiology and Cardiovascular Medicine

CiteScore

4.90

自引率

4.20%

发文量

期刊介绍： The journal ''Cardiovascular Diagnosis and Therapy'' (Print ISSN: 2223-3652; Online ISSN: 2223-3660) accepts basic and clinical science submissions related to Cardiovascular Medicine and Surgery. The mission of the journal is the rapid exchange of scientific information between clinicians and scientists worldwide. To reach this goal, the journal will focus on novel media, using a web-based, digital format in addition to traditional print-version. This includes on-line submission, review, publication, and distribution. The digital format will also allow submission of extensive supporting visual material, both images and video. The website www.thecdt.org will serve as the central hub and also allow posting of comments and on-line discussion. The web-site of the journal will be linked to a number of international web-sites (e.g. www.dxy.cn), which will significantly expand the distribution of its contents.