Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms

AbstractThe aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models—multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.

Authors
Shafiei S.B.1 , Shadpour Saeed2 , Mohler J.L. 3 , Sasangohar Farzan4 , Gutierrez Camille5 , Seilanian Toussi Mehdi1 , Shafqat Ambreen 1
Number of issue
6
Language
English
Pages
2963-2971
Status
Published
Volume
17
Year
2023
Organizations
  • 1 Intelligent Cancer Care Laboratory, Department of Urology
  • 2 University of Guelph
  • 3 Department of Urology
  • 4 Wm Michael Barnes and Department of Industrial and Systems Engineering at Texas A&M University
  • 5 Sisters of Charity Health System
Keywords
Blunt dissection; retraction; Burn dissection; hysterectomy; cystectomy; nephrectomy; robot-assisted surgery; expertise level; minimally invasive surgery; surgery; urology
Date of creation
01.07.2024
Date of change
01.07.2024
Short link
https://repository.rudn.ru/en/records/article/record/109957/
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