Assessment of valve regurgitation severity via contrastive learning and multi-view video integration

Objective. This paper presents a novel approach for addressing the intricate task of diagnosing aortic valve regurgitation (AR), a valvular disease characterized by blood leakage due to incompetence of the valve closure. Conventional diagnostic techniques require detailed evaluations of multi-modal clinical data, frequently resulting in labor-intensive and time-consuming procedures that are vulnerable to varying subjective assessment of regurgitation severity. Approach. In our research, we introduce the multi-view video contrastive network, designed to leverage multiple color Doppler imaging inputs for multi-view video processing. We leverage supervised contrastive learning as a strategic approach to tackle class imbalance and enhance the effectiveness of our feature representation learning. Specifically, we introduce a contrastive learning framework to enhance representation learning within the embedding space through inter-patient and intra-patient contrastive loss terms. Main results. We conducted extensive experiments using an in-house dataset comprising 250 echocardiography video series. Our results exhibit a substantial improvement in diagnostic accuracy for AR compared to state-of-the-art methods in terms of accuracy by 9.60%, precision by 8.67%, recall by 9.01%, and F 1-score by 8.92%. These results emphasize the capacity of our approach to provide a more precise and efficient method for evaluating the severity of AR. Significance. The proposed model could quickly and accurately make decisions about the severity of AR, potentially serving as a useful prescreening tool.