Focused cardiac ultrasound (FoCUS) is a medical technique that uses ultrasound to examine the heart at the bedside. To pave the way for an automatic analysis of FoCUS videos, the first step is to identify the imaged cardiac view. Previously developed methods have considered each frame separately and disregarded any temporal information, which is key due to FoCUS’ characteristic instability. Hereto, different strategies for video-level FoCUS view classification were investigated, all built on top of a ResNet-like network. In total, twelve networks were compared, from those sharing a spatial feature extractor to an architecture employing a spatiotemporal feature extractor. Overall, the latter led to the best performing model, highlighting a better ability to identify moving structures like heart valves, with a Matthews correlation coefficient of 0.9569.
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