Feasibility of an artificial intelligence based fractional flow reserve assessment for coronary artery disease.

IF 2 4区医学 Q3 CARDIAC & CARDIOVASCULAR SYSTEMS

Coronary artery disease Pub Date : 2026-05-05 DOI:10.1097/MCA.0000000000001647

Chun-Chin Chang, Song-Po Chen, Ya-Wan Lu, Wei-Ting Sung, Ting-Yung Chang, Ruey-Hsing Chou, Shu-Mei Guo, Po-Hsun Huang

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引用次数: 0

Abstract

Background: The implementation of artificial intelligence has been investigated in many aspects of cardiovascular disease.

Objectives: To develop deep learning models based on coronary angiograms to detect functionally significant coronary stenoses.

Methods: A total of 610 frames from 122 coronary arteries that received pressure wire-based fractional flow reserve (FFR) assessment were analyzed. Deep learning models were developed for the segmentation and classification of coronary stenoses. Both internal and external validation of the deep learning models were performed.

Results: The mean FFR value was 0.84 ± 0.08. The artificial intelligence-based FFR was significantly correlated with wire-based FFR with an average correlation coefficient of 0.68 and a mean absolute error of 0.05. The diagnostic performance of artificial intelligence-based FFR versus wire-based FFR was accuracy 87.6%, F1 score = 83.6%, and recall = 81.1%. The artificial intelligence-based FFR showed good discriminative performance with an area under the receiver operating characteristic curve of 86.5% (95% CI: 79.3-93.6).

Conclusion: The artificial intelligence-based FFR showed moderate agreement with pressure wire-based FFR and showed promising diagnostic performance in the internal cohort, although reduced performance was observed in external validation, warranting further refinement and multicenter validation.

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基于人工智能的冠状动脉疾病部分血流储备评估的可行性。

背景：人工智能的实施在心血管疾病的许多方面都得到了研究。目的：建立基于冠状动脉造影的深度学习模型，以检测功能显著的冠状动脉狭窄。方法：对122条冠状动脉接受压力丝法血流储备分数（FFR）评估的610例患者进行分析。开发了用于冠状动脉狭窄分割和分类的深度学习模型。对深度学习模型进行了内部和外部验证。结果：平均FFR值为0.84±0.08。基于人工智能的FFR与基于有线的FFR显著相关，平均相关系数为0.68，平均绝对误差为0.05。基于人工智能的FFR与基于线的FFR的诊断准确率为87.6%，F1评分为83.6%，召回率为81.1%。基于人工智能的FFR表现出良好的判别性能，接受者工作特征曲线下面积为86.5% （95% CI: 79.3-93.6）。结论：基于人工智能的FFR与基于压力丝的FFR表现出适度的一致性，并且在内部队列中显示出有希望的诊断性能，尽管在外部验证中观察到性能降低，需要进一步改进和多中心验证。

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

Coronary artery disease 医学-外周血管病

CiteScore

2.50

自引率

0.00%

发文量

190

审稿时长

6-12 weeks

期刊介绍： Coronary Artery Disease welcomes reports of original research with a clinical emphasis, including observational studies, clinical trials, translational research, novel imaging, pharmacology and interventional approaches as well as advances in laboratory research that contribute to the understanding of coronary artery disease. Each issue of Coronary Artery Disease is divided into four areas of focus: Original Research articles, Review in Depth articles by leading experts in the field, Editorials and Images in Coronary Artery Disease. The Editorials will comment on selected original research published in each issue of Coronary Artery Disease, as well as highlight controversies in coronary artery disease understanding and management. Submitted artcles undergo a preliminary review by the editor. Some articles may be returned to authors without further consideration. Those being considered for publication will undergo further assessment and peer-review by the editors and those invited to do so from a reviewer pool.