用深度学习自动计算心肌灌注帧数，快速客观评估微血管功能障碍。
Deep Learning-Based Automated Quantification of TIMI Myocardial Perfusion Frame Count (DL-TMPFC) from Coronary Angiography: A Novel Framework for Rapid Assessment of Microvascular Dysfunction
- 通过双阶段网络自动识别灌注区域并计算帧数。
- 与专家手动测量高度一致，偏差仅-0.93帧，相关系数达0.98。
- 适用于多种冠脉病变，支持连续风险分层，适合临床集成使用。
目的：冠状动脉微血管功能障碍（CMVD）影响约40%-60%缺血且冠脉无明显狭窄的患者，但诊断困难，依赖侵入性检查或主观的TIMI血流分级。TIMI心肌灌注帧数（TMPFC）提供了基于造影的客观量化指标，但其临床应用受限于手动计算繁琐及验证不足。本研究旨在开发并验证一种基于深度学习的TMPFC自动计算框架（DL-TMPFC），实现临床流程整合。方法与结果：该框架包含两部分：首先由狭窄检测网络排除阻塞性冠心病（CAD）；其次通过区域感知分割网络识别灌注区域，并自动确定造影序列中的首末帧。在来自三家独立机构的655例患者中进行验证（其中445例为阻塞性CAD，100例确诊CMVD，110例对照组）。DL-TMPFC与专家手动测量具有极佳一致性（偏倚：-0.93帧；95%一致性界限：-5.33至+3.47；相关系数r=0.98）。该方法显著提升临床可行性，实现全自动化且消除观察者差异。临床上，DL-TMPFC可准确识别不同冠脉病理下的CMVD，并捕捉其连续严重程度，支持定量风险分层。结论：DL-TMPFC实现了从常规造影中自动、标准化、精确量化CMVD，提供即时客观诊断信息，有助于临床及时识别和管理微血管功能障碍。
原文摘要 · Abstract (English)
Aims: Coronary microvascular dysfunction (CMVD) affects approximately 40%-60% of patients with ischemia and non-obstructive coronary arteries, yet diagnosis remains challenging due to reliance on invasive functional testing or subjective Thrombolysis In Myocardial Infarction (TIMI) flow grade. The TIMI Myocardial Perfusion Frame Count (TMPFC) offers an objective, angiography-based quantitative measure of CMVD, but its clinical translation is hindered by cumbersome manual calculation and insufficient validation. This study aims to develop and validate a deep learning-powered TMPFC calculation (DL-TMPFC), enabling integration into clinical workflows. Methods and results: DL-TMPFC framework comprised two components. A stenosis detection network first excluded obstructive coronary artery disease (CAD). A territory-aware segmentation network then identified perfusion territories and TMPFC calculation module automatically determined the first and last frames from angiographic sequences. The framework was validated in a cohort of 655 patients (445 of obstructive CAD, 100 of confirmed CMVD, 110 of control group) from three independent institutions. DL-TMPFC showed excellent agreement with expert manual measurements (bias: -0.93 frames; 95% LoA: -5.33 to +3.47; r =0.98). DL-TMPFC markedly enhanced clinical feasibility by fully automating TMPFC and removing observer dependence. Clinically, DL-TMPFC accurately identified CMVD across a full spectrum of coronary pathologies and captured the continuous severity of CMVD beyond binary classification, enabling quantitative risk stratification. Conclusion: DL-TMPFC enabled automatic, standardized, and accurate quantification of CMVD directly from routine angiography. By providing an automatic and objective measure, this tool provided immediate diagnostic information for timely recognition and management of CMVD in clinical practice.
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