Performance Prediction of Surgeon with Eye-Gaze Data for Robot-Assisted-Surgery

Robot assisted surgery is quite useful method for medical operations. To use this method properly, evaluation of surgeon with low experience has critical significance. Currently, evaluation is made by simulation programs that specified for robot assisted surgery or result of scoring with a survey made by expert surgeon who tracks all the session. Aim of this study is assessment of surgeons in real life robot-assisted operations with eye-tracker device without an evaluator. To make this evaluation, output with constructed regression models with derived features from an open source data that has been collecting during robot-assisted-surgery simulation is compared with score produced by simulation. Mean of root mean squared error of Xgboost that has 0.124 value and R2 score as 0.456 value were obtained for best model output.