Developing an Effective Off-the-job Training Model and an Automated Evaluation System for Thoracoscopic Esophageal Atresia Surgery

Abstract

Pediatric minimally invasive surgery requires advanced technical skills. Off-the-job training (OJT), especially when using disease-specific models, is an effective method of acquiring surgical skills. To achieve effective OJT, it is necessary to provide objective and appropriate skill assessment feedback to trainees. We aimed to construct a system that automatically evaluates surgical skills based on forceps movement using deep learning (DL).

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