Key result
The ECHO-Attention network achieved an overall accuracy of 94.81% for classifying echocardiographic standard views on entire video samples, improving upon previous baseline models.
Why the study?
Determining probe viewpoint is essential for automatic echocardiogram analysis, but video-level classification remains complicated by challenges in distinguishing adjacent views.
Absolute Event Rate: 94.81% vs 94.3%
A novel hybrid attention-based deep learning architecture (ECHO-Attention) achieves high accuracy (94.81%) in automatically classifying standard echocardiographic views, including anatomically similar adjacent views.
May aid automated echo view classification; leaves open clinical utility pending prospective validation.
The determination of the probe viewpoint forms an essential step in automatic echocardiographic image analysis. However, classifying echocardiograms at the video level is complicated, and previous observations concluded that the most significant challenge lies in distinguishing among the various adjacent views. To this end, we propose an ECHO-Attention architecture consisting of two parts. We first design an ECHO-ACTION block, which efficiently encodes Spatio-temporal features, channel-wise features, and motion features. Then, we can insert this block into existing ResNet architectures, combined with a self-attention module to ensure its task-related focus, to form an effective ECHO-Attention network. The experimental results are confirmed on a dataset of 2693 videos acquired from 267 patients that trained cardiologist has manually labeled. Our methods provide a comparable classification performance (overall accuracy of 94.81%) on the entire video sample and achieved significant improvements on the classification of anatomically similar views (precision 88.65% and 81.70% for parasternal short-axis apical view and parasternal short-axis papillary view on 30-frame clips, respectively).
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Ye et al. (2022) studied Echocardiographic view classification (n=267). ECHO-Attention network vs. Baseline deep learning models (e.g., Xception+BiLSTM) was evaluated on Overall accuracy on entire video sample. The ECHO-Attention network achieved an overall accuracy of 94.81% for classifying echocardiographic standard views on entire video samples, improving upon previous baseline models.
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