Artificial intelligence (AI) is rarely directly used in patent foramen ovale (PFO) diagnosis. In this study, an AI model was developed to detect the presence of PFO automatically in both contrast transthoracic echocardiography (cTTE) images and videos. The whole intelligent diagnosis neural network framework consists of two functional modules of image segmentation (Unet, = 1866) and image classification (ResNet 101, = 9152). Finally, another test databases, including 20 cTTE videos (4609 cTTE images), was used to compare the RLS classification model accuracy between AI model and different levels of physicians. The Dice similarity coefficient of left chamber segmentation model of cTTE images was 91.41%, the accuracy of PFO-RLS classification model of cTTE images was 83.55%, the accuracy of PFO-RLS classification model of cTTE videos was 90%. Besides, the AI diagnosis time was significantly shorter than doctors (at only 1.3 s).
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| http://dx.doi.org/10.1016/j.isci.2024.111012 | DOI Listing |
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