Abstract:Objectives: The clinical workflow of the second trimester anomaly scan is not well studied and holds potential for efficiency improvement. We aimed to create a model for automatic anatomical description of full-length fetal anomaly scan videos using artificial intelligence. Methods: We prospectively recorded routine full-length second trimester anomaly scans, extracted short clips of important scan events by detecting video freeze, and image/clip save. For machine learning, we created a training dataset by man… Show more
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