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A High-Speed Image AI Facilitating the Visual Assessment of the Membrane's Motion in EXCOR VAD
Hidehito Ota1, Takuya Kawahara2, Miki Ohta2
1Department of Pediatrics, The University of Tokyo Hospital, Tokyo, Japan.
Background:
Visual assessment of membrane motion is essential for managing EXCOR VAD, but accuracy depends on observer experience. We evaluated a high-speed image AI model to support healthcare providers.
Methods:
Patients on EXCOR Pediatric admitted to the University of Tokyo Hospital (May 2022-May 2024) were included. Membrane images were obtained from patients and a manually filled pump at bench. An image recognition model was trained to estimate membrane position. Experienced physicians (N = 11) and inexperienced physicians (N = 11) assessed pump status in a sample dataset (N = 12) with and without AI assistance.
Results:
A total of 142 movies from five patients were collected (98 training, 45 validation), plus 1100 bench images for training. Model accuracy was 0.91, with AUROCs of 0.99 ("fill") and 0.96 ("empty"). Among experienced physicians, accuracy significantly improved with AI assistance from 0.83 (0.67-0.88) to 0.92 (0.92-1.0) (median (IQR); p = 0.016). Among inexperienced physicians, accuracy also significantly improved with AI assistance from 0.67 (0.5-0.75) to 0.83 (0.75-0.92) (p = 0.049).
Conclusion:
A high-speed image AI can facilitate the visual assessment of EXCOR VAD by healthcare providers.
