Video Clip Extraction From Fetal Ultrasound Scans Using Artificial Intelligence to Allow Remote Second Expert Review for Congenital Heart Disease

Abstract

OBJECTIVE: To use artificial intelligence (AI) to automatically extract video clips of the fetal heart from a stream of ultrasound video, and to assess the performance of these when used for remote second review. METHODS: Using a dataset from a previous clinical trial of AI to assist in fetal ultrasound scanning, AI was used to automatically extract video clips of the fetal heart from ultrasound scans of 48 fetuses in which the diagnosis was known: 24 normal and 24 with congenital heart disease (CHD). These, and manually still saved images, were shown in a random order to expert clinicians, who were asked to detect cardiac abnormalities. RESULTS: The initial manual scan had a sensitivity of 0.792 and specificity of 0.917 for detecting CHD in this cohort. The addition of second review improved the sensitivity to 0.975 using video clips, which was significantly higher than using still images (0.892, p = 0.002). There was a significant drop in specificity to 0.767 and 0.833 (p < 0.001) for the video and still method, respectively, which were statistically similar to each other (p = 0.117). The median review time was 1.0 min (IQR 0.71) for the still images, and 3.75 min (IQR 3.12) for the AI-generated video clips. CONCLUSION: AI can be used to automatically extract fetal cardiac video clips, and these can be used for remote second review to improve detection rates. Video clips are superior to still images, but both methods result in a significant drop in specificity.

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