Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated...

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Bibliographic Details
Published inarXiv.org
Main Authors Fard, Mahtab J, Ameri, Sattar, Chinnam, Ratna B, Pandya, Abhilash K, Klein, Michael D, Ellis, R Darin
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.11.2016
Subjects
Artificial intelligence
Classification
Completion time
Curvature
Ground truth
Machine learning
Robotic surgery
Smoothness
Space perception
Surgeons
Surgery
Test procedures
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Summary:Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.
ISSN:2331-8422