SECNet: Differentiable Mask-Driven Spatiotemporal Learning for Video-Based Spontaneous Echo Contrast Severity Detection

Spontaneous Echo Contrast (SEC), characterized by smoke-like dynamic echogenicity in trans esophageal echocardiography (TEE), serves as a critical imaging biomarker for assessing thromboembolic risk. However, current diagnostic approaches depend on manual interpretation or regression-based algorithms, which are labor-intensive, prone to inconsistency, and limited in generalizability. To address these challenges, we propose SECNet, an end-to-end deep learning framework for auto mated SEC detection in TEE videos.…