Mahtab J Fard1, Sattar Ameri1, R Darin Ellis1, Ratna B Chinnam1, Abhilash K Pandya2, Michael D Klein3. 1. Department of Industrial and Systems Engineering, Wayne State University, Detroit, Michigan, USA. 2. Department of Electrical and Computer Engineering, Wayne State University, Detroit, Michigan, USA. 3. Department of Surgery, Wayne State University School of Medicine and Pediatric Surgery, Children's Hospital of Michigan, Detroit, Michigan, USA.
Abstract
BACKGROUND: Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise. METHODS: Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied. RESULTS: The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task. CONCLUSION: This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.
BACKGROUND: Surgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot-assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise. METHODS: Eight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise - novice and expert. Three classification methods - k-nearest neighbours, logistic regression and support vector machines - are applied. RESULTS: The result shows that the proposed framework can classify surgeons' expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task. CONCLUSION: This study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.
Authors: Joshua A Lee; Michaela F Close; Yuan F Liu; M Andrew Rowley; Mitchell J Isaac; Mark S Costello; Shaun A Nguyen; Ted A Meyer Journal: JAMA Otolaryngol Head Neck Surg Date: 2020-10-01 Impact factor: 6.223
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