Monitoring Berlin Heart EXCOR by computer vision: A preliminary implementation and evaluation

Abstract

AbstractBackgroundEXCOR Pediatric is one of the most commonly used ventricular assist devices (VAD) for small children; it requires visual inspection of the diaphragm movement to assess its operating status. Although this visual inspection can only be performed by trained medical professionals, it can also be attempted by the recent advances in computer vision technology.MethodsMovement of the diaphragm in the operating EXCOR VAD was recorded as movies and annotated frame‐by‐frame in three classes according to the state of the diaphragm: “fill,” “mid,” and “empty.” Three models, MobileNetV3, EfficientNetV2, and MobileViT, were trained using the frames, and their performance was evaluated based on the accuracy and area under the receiver operating characteristic curve (AROC).ResultsA total of 152 movies were available from two participants. Only the 10 mL pumps were used. Ninety‐eight movies were used for annotation and frame extraction, and 7949 frames per class were included in the analysis. The macro‐average accuracies of the three models were 0.88, 0.91, and 0.93, and the AROC were 0.99, 0.98, and 0.99 for MobileNetV3, EfficientNetV2, and MobileViT, respectively.ConclusionImage recognition models based on lightweight deep neural networks could detect the diaphragm state of EXCOR VAD with sufficient accuracy, although there were limited variations in the dataset. This suggests the potential of computer vision for the automated monitoring of the EXCOR diaphragm position.