Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks

TL;DR

This paper presents a fully convolutional neural network that accurately classifies and interprets surgical skills from kinematic data, providing automatic, objective feedback to enhance surgical training and practice.

Contribution

The study introduces an interpretable CNN model for surgical skill evaluation that combines high accuracy with explainability using class activation maps.

Findings

Achieved state-of-the-art performance on JIGSAWS dataset

Enabled automatic identification of influential surgical motion segments

Provided personalized feedback for surgical skill improvement

Abstract

Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. Methods: In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. Results: Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. Conclusions: This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts.