Evaluating surgical skills from kinematic data using convolutional neural networks

TL;DR

This paper presents a CNN-based method for automatic surgical skill assessment using kinematic data, achieving high accuracy and providing explainability through class activation maps, thus aiding surgical training and evaluation.

Contribution

The study introduces a CNN approach for surgical skill evaluation that combines high accuracy with interpretability, enhancing automated assessment and feedback.

Findings

Achieved 100% accuracy on suturing and needle passing tasks.

Provided explainability via class activation maps for skill prediction.

Validated on the JIGSAWS dataset with competitive results.

Abstract

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.