Related Experiment Video
Updated: Jun 20, 2025
Semi-automated Optical Heartbeat Analysis of Small Hearts
Published on: September 16, 2009
Monitoring Berlin Heart EXCOR by computer vision: A preliminary implementation and evaluation
Hidehito Ota1, Tomomi Fujimura1, Ayumi Kunikata1
1Department of Pediatrics, School of Medicine, The University of Tokyo, Bunkyo, Japan.
Background:
EXCOR 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.
Methods:
Movement 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).
Results:
A 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.
Conclusion:
Image 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.
