2025-08-06 マウントサイナイ医療システム (MSHS)
ChatGPT:
<関連情報>
- https://www.mountsinai.org/about/newsroom/2025/mount-sinai-researchers-develop-promising-aidriven-surgical-education-model-to-improve-quality-of-resident-training
- https://www.liebertpub.com/doi/10.1177/29941520251361898
深層学習と拡張現実を組み合わせた外科訓練用の自律型教育システム Autonomous Educational System for Surgical Training Utilizing Deep Learning Combined with Extended Reality
Jonathan J. Stone, Nelson N. Stone, Steven H. Griffith, Kyle Zeller, and Michael P. Wilson
Journal of Medical Extended Reality Published:23 July 2025
DOI:https://doi.org/10.1089/10.1177/29941520251361898
Abstract
Purpose: Artificial intelligence (AI) algorithms created with machines and deep learning combined with an extended reality (XR) headset could help train physicians in new technology without the need for the presence of an instructor.
Materials and Methods: A partial nephrectomy phantom was created from 3D-printed casts, which were designed from an anonymized patient’s computerized tomography scan. The casts were filled with water-based polymers and assembled to create the partial nephrectomy phantom. The students wore a custom-designed XR headset, where instructions were streamed to train and observe them while placing a bulldog clamp on the phantom’s renal artery. Machine learning models were developed from four states (clamp on artery, vein, ureter, and no structure), which were used to create the educational system for instructorless surgical training (ESIST). Customized deep learning architecture was deployed in real time to analyze the video feeds and determine user progress. The computer determined one of four classes based on the object clamped to simulate real circumstances, provide appropriate instructions, and track errors. Seventeen participants completed a 19-question survey for educational value and usability after performing the procedure.
Results: High algorithm performance was confirmed using confusion matrix scores, which achieved an accuracy of 99.91% for placement of the bulldog clamp on the renal artery. Survey responses were strongly disagree—1 (0.3%), disagree—46 (15.7%), agree—139 (47.4%), and strongly agree—107 (36.6%) with the value of ESIST. The responses were converted to a 2-point scale and reported as favorable in 84% of the 19 questions (range 47.5–100%).
Conclusions: We introduced an AI system to train surgeons to place a clamp on the renal artery using a kidney phantom while wearing an XR headset. This investigation suggests AI could assist in surgical education, potentially offer a means to monitor procedural progress, and provide a pathway for autonomous learning.
