Scholarlink

<p>Coronary heart disease (CAD) is the leading cause of death worldwide, and coronary angiography (CAG) serves as the gold standard for its assessment. Valvular heart diseases, such as severe aortic stenosis (AS) and severe mitral regurgitation (MR), frequently coexist with CAD yet are often underdiagnosed. Opportunistic screening for these conditions at the time of CAG could influence therapeutic strategies and improve prognosis. This study developed and validated a foundation model for the automated screening of severe AS and severe MR from CAG videos. The study presents CAGFound, a video-based foundation model that was self-supervised pre-trained on CAG sequences from seven medical centers and subsequently adapted to two downstream tasks: screening for severe AS and severe MR. Two internal and external validation datasets were retrospectively enrolled from the First Medical Center and the Sixth Medical Center of Chinese PLA General Hospital, respectively. A total of 117,383 unlabeled CAG sequences were used to build CAGFound. For the detection of severe AS, CAGFound achieved an area under the receiver operating characteristic curve (AUROC) of 0.932 (sensitivity 0.767, specificity 0.921) on the internal test dataset and maintained robust performance on the external validation dataset, with an AUROC of 0.879 (sensitivity 0.800, specificity 0.955). For the detection of severe MR, the model demonstrated an AUROC of 0.933 (sensitivity 0.738, specificity 0.938) on the internal dataset and an AUROC of 0.896 (sensitivity 0.754, specificity 0.855) on the external cohort. The performance of CAGFound was also compared with other video-based foundation models, VideoMAEv2 and Video Swin. CAGFound achieved the highest AUROC and demonstrated the best calibration performance (Brier score 0.122, R<sup>2</sup> 0.478) compared with VideoMAEv2 (Brier score 0.159, R<sup>2</sup> 0.306) and Video Swin (Brier score 0.162, R<sup>2</sup> 0.306). CAGFound enables accurate, automated screening for severe AS and severe MR during CAG. It has the potential to increase detection rates, facilitate timely clinical referral, and improve prognosis without requiring additional contrast administration or procedures.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Foundation model for screening severe mitral regurgitation and severe aortic stenosis from coronary angiograms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月17日

查看全文
  • Yingqian Zhang,
  • Zhiming Shao,
  • Zechen Wei,
  • Changfu Liu,
  • Zeyu Sun,
  • Liangliang Liu,
  • Ran Xin,
  • Yong Ge,
  • Chen Wang,
  • Guodong Ding,
  • Lei Gao,
  • Liwei Zhang,
  • Hui Hui,
  • Jie Tian,
  • Yundai Chen

摘要

Coronary heart disease (CAD) is the leading cause of death worldwide, and coronary angiography (CAG) serves as the gold standard for its assessment. Valvular heart diseases, such as severe aortic stenosis (AS) and severe mitral regurgitation (MR), frequently coexist with CAD yet are often underdiagnosed. Opportunistic screening for these conditions at the time of CAG could influence therapeutic strategies and improve prognosis. This study developed and validated a foundation model for the automated screening of severe AS and severe MR from CAG videos. The study presents CAGFound, a video-based foundation model that was self-supervised pre-trained on CAG sequences from seven medical centers and subsequently adapted to two downstream tasks: screening for severe AS and severe MR. Two internal and external validation datasets were retrospectively enrolled from the First Medical Center and the Sixth Medical Center of Chinese PLA General Hospital, respectively. A total of 117,383 unlabeled CAG sequences were used to build CAGFound. For the detection of severe AS, CAGFound achieved an area under the receiver operating characteristic curve (AUROC) of 0.932 (sensitivity 0.767, specificity 0.921) on the internal test dataset and maintained robust performance on the external validation dataset, with an AUROC of 0.879 (sensitivity 0.800, specificity 0.955). For the detection of severe MR, the model demonstrated an AUROC of 0.933 (sensitivity 0.738, specificity 0.938) on the internal dataset and an AUROC of 0.896 (sensitivity 0.754, specificity 0.855) on the external cohort. The performance of CAGFound was also compared with other video-based foundation models, VideoMAEv2 and Video Swin. CAGFound achieved the highest AUROC and demonstrated the best calibration performance (Brier score 0.122, R2 0.478) compared with VideoMAEv2 (Brier score 0.159, R2 0.306) and Video Swin (Brier score 0.162, R2 0.306). CAGFound enables accurate, automated screening for severe AS and severe MR during CAG. It has the potential to increase detection rates, facilitate timely clinical referral, and improve prognosis without requiring additional contrast administration or procedures.