Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.

Journal: Surgical endoscopy

PMID: 39039296

Abstract

BACKGROUND: Objective and standardized evaluation of surgical skills in robot-assisted surgery (RAS) holds critical importance for both surgical education and patient safety. This study introduces machine learning (ML) techniques using features derived from electroencephalogram (EEG) and eye-tracking data to identify surgical subtasks and classify skill levels.

Authors

Somayeh B Shafiei

Department of Mechanical and Aerospace Engineering, Human in the Loop System Laboratory, University at Buffalo, Buffalo, NY.
Saeed Shadpour

Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, Ontario, Canada.
James L Mohler

Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
Eric C Kauffman
Matthew Holden

Carleton University, Ottawa, K1S 5B6, Canada.
Camille Gutierrez

Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY, 14214, USA.

Keywords

Animals Clinical Competence Electroencephalography Eye-Tracking Technology Humans Machine Learning Reproducibility of Results Robotic Surgical Procedures Swine

External Resources

View on PubMed Access via DOI PubMed (39039296)

Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning.

Abstract

Authors

Keywords

External Resources

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