SynSeq：基于冠状动脉造影视频的端到端SYNTAX评分预测
SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos
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中文总结 AI 辅助
SynSeq提出一种基于视频的端到端方法，直接预测冠状动脉造影的SYNTAX评分，通过零膨胀损失和线性缩放显著提升准确性，降低偏差，并支持血运重建决策。
中文摘要 AI 辅助
SYNTAX评分是评估冠状动脉疾病和指导血运重建治疗决策的既定工具。然而，由临床专家从冠状动脉造影视频中手动估算该评分既耗时又存在阅片者间差异。尽管机器学习在自动化这一过程中展现出潜力，但先前的工作主要集中在病变检测、特征描述或二元疾病分类上，直接进行SYNTAX评分预测的研究相对较少。我们提出SynSeq，一种基于视频的直接SYNTAX评分预测方法。它结合了针对性的预处理和定制的训练策略，使用零膨胀感知损失和线性目标缩放。在公开的CardioSyntax数据集上评估，SynSeq显著优于先前的最先进方法，将$R^2$提高了0.55，预测偏差降低了93.1%，并在三位独立专家评分者的标注中实现了更一致的性能。此外，SynSeq在血运重建治疗建议上达到了0.80的加权$F_1$分数，略低于专家间一致性。这些结果表明SynSeq在提供一致、自动化的SYNTAX评分评估和可靠的冠状动脉血运重建规划决策支持方面具有潜力。
英文摘要
The SYNTAX score is an established tool for assessing coronary artery disease and guiding revascularization treatment decisions. However, its manual estimation from coronary angiography videos by clinical experts is time-consuming and subject to inter-reader variability. While machine learning has shown promise in automating this process, prior work has primarily focused on lesion detection, characterization, or binary disease classification, leaving direct SYNTAX score prediction relatively unexplored. We propose SynSeq, a video-based method for direct SYNTAX score prediction. It combines targeted preprocessing with a tailored training strategy using a zero-inflation-aware loss and linear target scaling. Evaluated on the public CardioSyntax dataset, SynSeq significantly outperforms previous state-of-the-art methods, improving $R^2$ by 0.55, reducing prediction bias by 93.1% and achieving more consistent performance across annotations from three independent expert graders. In addition, SynSeq achieves a weighted $F_1$-score of 0.80 for revascularization treatment recommendations, slightly below inter-expert agreement. These results demonstrate the potential of SynSeq to provide consistent, automated SYNTAX score assessment and reliable decision support for coronary revascularization planning.
发表机构
- Medical University of Vienna（维也纳医科大学）
机构由 AI 辅助整理，请以论文原文为准。
