Mitral regurgitation (MR) is a serious heart valve disease. Early and accurate diagnosis of MR via ultrasound video is critical for timely clinical decision-making and surgical intervention. However, manual MR diagnosis heavily relies on the operator's experience, which may cause misdiagnosis and inter-observer variability. Since MR data is limited and has large intra-class variability, we propose an unsupervised out-of-distribution (OOD) detection method to identify MR rather than building a deep classifier. To our knowledge, we are the first to explore OOD in MR ultrasound videos. Our method consists of a feature extractor, a feature reconstruction model, and a residual accumulation amplification algorithm. The feature extractor obtains features from the video clips and feeds them into the feature reconstruction model to restore the original features. The residual accumulation amplification algorithm then iteratively performs noise feature reconstruction, amplifying the reconstructed error of OOD features. This algorithm is straightforward yet efficient and can seamlessly integrate as a plug-and-play component in reconstruction-based OOD detection methods. We validated the proposed method on a large ultrasound dataset containing 893 non-MR and 267 MR videos. Experimental results show that our OOD detection method can effectively identify MR samples.

翻译：二尖瓣反流（MR）是一种严重的心脏瓣膜疾病。通过超声视频早期准确诊断MR对于及时临床决策和手术干预至关重要。然而，人工MR诊断高度依赖操作者经验，可能导致误诊和观察者间差异。由于MR数据有限且类内差异较大，我们提出了一种无监督分布外（OOD）检测方法来识别MR，而非构建深度分类器。据我们所知，我们是首个在MR超声视频中探索OOD检测的研究。我们的方法包含特征提取器、特征重建模型和残差累积放大算法。特征提取器从视频片段中获取特征，并将其输入特征重建模型以恢复原始特征。残差累积放大算法随后迭代执行噪声特征重建，放大OOD特征的重建误差。该算法简洁高效，可作为即插即用模块无缝集成到基于重建的OOD检测方法中。我们在包含893个非MR和267个MR视频的大型超声数据集上验证了所提方法。实验结果表明，我们的OOD检测方法能有效识别MR样本。