用超声心动图视频实现精准可解释的主动脉瓣诊断
Robust and Explainable Bicuspid Aortic Valve Diagnosis Using Stacked Ensembles on Echocardiography
- 多模型视频集成学习，从常规超声视频中识别主动脉瓣类型
- 在90例患者数据上达到0.907的F1分数和0.877的召回率
- 通过热力图与贡献度分析，让AI决策过程透明可审计
经胸超声心动图（TTE）是诊断二尖瓣主动脉瓣（BAV）的首选影像方式，但其诊断效果受操作者水平和图像质量影响。本研究基于常规获取的胸骨旁长轴（PLAX）动态影像，构建了一个可解释的AI模型，用于区分BAV与三尖瓣主动脉瓣（TAV）。采用多骨干视频集成模型，在90例患者研究（48例BAV，42例TAV）上，通过泄漏感知、分层外交叉验证协议进行训练与评估。在固定外层划分和10个随机种子下，校准后的堆叠集成模型达到外交叉验证F1分数0.907，召回率0.877。帧级Grad-CAM将显著特征定位至主动脉根部和瓣叶平面，全局聚合的SHAP值量化了各视频骨干对最终预测的贡献，实现了病例级透明审计。结果表明，基于PLAX的视频集成模型可从常规超声心动图中可靠分类BAV/TAV，有望在非专科或资源有限的临床环境中实现早期筛查。
原文摘要 · Abstract (English)
Transthoracic echocardiography (TTE) is the first-line imaging modality for diagnosing bicuspid aortic valve (BAV), yet diagnostic performance varies with operator expertise and image quality. We developed an explainable AI model that distinguishes BAV from tricuspid aortic valves (TAV) using routinely acquired parasternal long-axis (PLAX) cine loops. A multi-backbone video ensemble was trained and evaluated using a leakage-aware, stratified outer cross-validation protocol on $N{=}90$ patient studies (48 BAV, 42 TAV). Across fixed outer splits and 10 random seeds, the calibrated stacked ensemble achieved an outer-CV F1-score of $0.907$ and recall of $0.877$. Frame-level Grad-CAM localized salient evidence to the aortic root and leaflet plane, while globally aggregated SHAP values quantified each video backbone's contribution to the stacked prediction, enabling transparent, case-level auditability. These findings indicate that PLAX-based video ensembles can support reliable BAV/TAV classification from routine echocardiographic cine loops and may facilitate earlier detection in non-specialist or resource-limited clinical settings.
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