一站式血管造影分析系统，自动评估狭窄并预测支架效果。
Integrated Pipeline for Coronary Angiography With Automated Lesion Profiling, Virtual Stenting, and 100-Vessel FFR Validation
- 基于深度学习的全流程自动分析，整合病变检测与功能评估。
- 100支血管验证，与侵入式FFR相关性达0.89，准确率93%。
- 支持虚拟支架植入，快速生成血流动力学预测，适合临床决策辅助。
冠状动脉造影是评估冠心病的主要工具，但狭窄程度的视觉分级变异大且与缺血关联较弱。有创分数流量储备（FFR）虽能改善病变选择，却未被广泛使用。基于造影的定量血流比（QFR）可实现无导丝生理评估，但多数工具流程繁琐且独立于自动化解剖分析和虚拟经皮冠状动脉介入（PCI）规划。我们开发了AngioAI-QFR，一个仅依赖造影的端到端流程，结合深度学习进行狭窄检测、管腔分割、中心线与直径提取、每毫米相对血流容量（RFC）分析，以及虚拟支架植入后自动重算的造影衍生QFR。系统在100例连续血管中以有创FFR为参考进行评估。主要终点包括与FFR的一致性（相关性、平均绝对误差）及对FFR≤0.80的诊断性能。在保留帧上，狭窄检测精确率0.97，管腔分割Dice系数0.78。在100支血管中，AngioAI-QFR与FFR相关性为0.89，平均绝对误差0.045；检测FFR≤0.80的AUC为0.93，灵敏度0.88，特异性0.86。93%的血管可全自动完成，中位结果时间41秒。RFC分析可区分局灶性与弥漫性血流容量损失，虚拟支架植入预测局灶性病变的QFR提升大于弥漫性病变。AngioAI-QFR提供了一种实用、近实时的综合方案，融合计算机视觉、功能分析与虚拟PCI规划，实现自动化造影生理评估。
原文摘要 · Abstract (English)
Coronary angiography is the main tool for assessing coronary artery disease, but visual grading of stenosis is variable and only moderately related to ischaemia. Wire based fractional flow reserve (FFR) improves lesion selection but is not used systematically. Angiography derived indices such as quantitative flow ratio (QFR) offer wire free physiology, yet many tools are workflow intensive and separate from automated anatomy analysis and virtual PCI planning. We developed AngioAI-QFR, an end to end angiography only pipeline combining deep learning stenosis detection, lumen segmentation, centreline and diameter extraction, per millimetre Relative Flow Capacity profiling, and virtual stenting with automatic recomputation of angiography derived QFR. The system was evaluated in 100 consecutive vessels with invasive FFR as reference. Primary endpoints were agreement with FFR (correlation, mean absolute error) and diagnostic performance for FFR <= 0.80. On held out frames, stenosis detection achieved precision 0.97 and lumen segmentation Dice 0.78. Across 100 vessels, AngioAI-QFR correlated strongly with FFR (r = 0.89, MAE 0.045). The AUC for detecting FFR <= 0.80 was 0.93, with sensitivity 0.88 and specificity 0.86. The pipeline completed fully automatically in 93 percent of vessels, with median time to result 41 s. RFC profiling distinguished focal from diffuse capacity loss, and virtual stenting predicted larger QFR gain in focal than in diffuse disease. AngioAI-QFR provides a practical, near real time pipeline that unifies computer vision, functional profiling, and virtual PCI with automated angiography derived physiology.
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