  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.
