Developing surgical skill level classification model using visual metrics and a gradient boosting algorithm.

ABSTRACT:

Objective

Assessment of surgical skills is crucial for improving training standards and ensuring the quality of primary care. This study aimed to develop a gradient boosting classification model (GBM) to classify surgical expertise into inexperienced, competent, and experienced levels in robot-assisted surgery (RAS) using visual metrics.

Methods

Eye gaze data were recorded from 11 participants performing four subtasks; blunt dissection, retraction, cold dissection, and hot dissection using live pigs and the da Vinci robot. Eye gaze data were used to extract the visual metrics. One expert RAS surgeon evaluated each participant's performance and expertise level using the modified Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The extracted visual metri

SUBMITTER: Shafiei SB 

PROVIDER: S-EPMC10249659 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature