Key result
Gesture-level analysis using machine learning algorithms achieved 100% accuracy in classifying surgeons into novice and experienced groups for suturing, knot-tying, and needle-passing tasks.
Why the study?
Traditional surgical skill assessment methods are prone to bias and require substantial human resources, motivating automated assessment using Surgical Data Science.
Machine learning algorithms applied to kinematic data from robot-assisted minimally invasive surgery can accurately classify surgeons' skill levels, particularly when analyzed at the gesture level.
This is the authors' abstract. We don't add key points for this paper.
Improved surgical skills play a crucial role in ensuring optimal patient outcomes.Traditional methods for skill assessment include self-rating questionnaires and expert evaluations, but these approaches are prone to bias and require substantial qualified human resources.The emergence of Surgical Data Science (SDS) offers a promising avenue for automating skill assessment, leveraging data science techniques to capture, organize, analyze, and model surgical data.In this paper, kinematic data was employed from the JIGSAWS -which is the only skill-annotated Robot-Assisted Minimally Invasive Surgery (RAMIS) dataset -to classify surgeons into novice and experienced groups, using various classification methods (Decision Tree, k-Nearest Neighbors, Support Vector Machines, Logistic Regression, Dynamic Time Warping, and 1D Convolutional Neural Network).The research encompasses a thorough analysis of parameter tuning and dimensional reduction techniques with the aim of establishing a universal benchmark for data classification.The surgical training tasks of suturing, knot-tying and needle-passing consistently achieved 100 % accuracy.The accuracy attained during surgical gesture analysis often exceeded the overall accuracy of the global assessment of the dataset.
May aid objective skill assessment in robotic surgery training; hypothesis-generating pending larger prospective validation.
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Lukács et al. (2023) studied Robot-Assisted Minimally Invasive Surgery (RAMIS) skill assessment (n=8). Machine learning classification algorithms (Decision Tree, k-NN, SVM, Logistic Regression, DTW, 1D CNN) vs. Global dataset assessment was evaluated on Classification accuracy of surgeon skill level (novice vs. experienced). Gesture-level analysis using machine learning algorithms achieved 100% accuracy in classifying surgeons into novice and experienced groups for suturing, knot-tying, and needle-passing tasks.
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