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Automated robot‐assisted surgical skill evaluation: Predictive analytics approach
Author(s) -
Fard Mahtab J.,
Ameri Sattar,
Darin Ellis R.,
Chinnam Ratna B.,
Pandya Abhilash K.,
Klein Michael D.
Publication year - 2018
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1850
Subject(s) - computer science , task (project management) , artificial intelligence , support vector machine , robot , trajectory , knot tying , machine learning , logistic regression , movement assessment , movement (music) , human–computer interaction , motor skill , surgery , medicine , physics , management , astronomy , psychiatry , economics , philosophy , aesthetics
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.