系统评估513篇AI分析冠脉OCT文献，指出多数模型因方法缺陷难用于临床。
Review and Recommendations for using Artificial Intelligence in Intracoronary Optical Coherence Tomography Analysis
- 系统梳理2015-2023年513篇AI诊断冠脉病论文
- 仅35篇通过质量筛选，多数模型存在方法缺陷
- 提出改进方向，助力可临床应用的AI模型研发
人工智能（AI）在从血管内光学相干断层扫描（IVOCT）图像中快速准确诊断冠状动脉疾病（CAD）方面具有巨大潜力。尽管已有大量论文报道基于AI的诊断模型，但尚不清楚哪些模型具备临床实用价值且经过充分验证。本研究系统回顾了2015年1月至2023年2月期间发表的相关文献。检索共发现5,576项研究，初筛后纳入513项，经质量评估最终纳入35项进行综述。结果显示，大多数已识别模型目前不适用于临床，主要因方法学缺陷和潜在偏差。为此，我们提出了改进模型质量和研究实践的建议，以推动具备临床应用价值的AI产品发展。
原文摘要 · Abstract (English)
Artificial intelligence (AI) methodologies hold great promise for the rapid and accurate diagnosis of coronary artery disease (CAD) from intravascular optical coherent tomography (IVOCT) images. Numerous papers have been published describing AI-based models for different diagnostic tasks, yet it remains unclear which models have potential clinical utility and have been properly validated. This systematic review considered published literature between January 2015 and February 2023 describing AI-based diagnosis of CAD using IVOCT. Our search identified 5,576 studies, with 513 included after initial screening and 35 studies included in the final systematic review after quality screening. Our findings indicate that most of the identified models are not currently suitable for clinical use, primarily due to methodological flaws and underlying biases. To address these issues, we provide recommendations to improve model quality and research practices to enhance the development of clinically useful AI products.
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