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. SECNet incorporates two key components: (1) a region-of-interest segmentation module that accurately localizes SEC-prone cardiac structures while suppressing surrounding noise; and (2) a spa tiotemporal feature extraction module that captures both temporal dynamics and spatial distribution of SEC. These modules are linked through a novel differentiable mask guided bridging mechanism that enables joint optimization of localization and representation learning. Trained on a clinical dataset of 1,106 annotated TEE videos, SECNet achieves 93.7% accuracy and a 90.0% F1-score, outper forming the compared methods by 3.1-23.3 percentage points in accuracy. An auxiliary experiment on EchoNet Dynamic further demonstrates the applicability of the architecture to a related echocardiographic video-analysis task. This study supports standardized and reproducible SEC assessment to assist thromboembolic risk evaluation in clinical workflows.