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Updated: Sep 16, 2025
A Teleoperated Robotic System-Assisted Percutaneous Transiliac-Transsacral Screw Fixation Technique
Published on: January 6, 2023
Physiological Detection of Intraoperative Errors During Robot-Assisted Surgery
Christopher D'Ambrosia1, Estella Y Huang2, Nicole H Goldhaber2
1College of Physicians and Surgeons, Columbia University, New York, New York, USA.
Background:
This study tested the measurement of operator physiology during performance on robot-assisted surgery simulations to determine if these signals can identify errors and classify high and low performers.
Methods:
57 participants performed digital simulations on da Vinci Xi system. Simulation videos, electrocardiogram (EKG), and electroencephalography (EEG) were analysed using linear mixed effects models.
Results:
Relative to non-error intervals, errors elicited significant differences in EKG and EEG measures, including high-frequency power, interbeat interval and ratio of theta-to-alpha EEG power. High and low performers differed significantly in several of these measures, while classification models were accurate for the detection of errors (85.7%) and performance groups (96.3%), and using physiological signals leading up to errors, could accurately predict upcoming errors (85.7%).
Conclusions:
Noninvasive recording of physiology can differentiate error from non-error intervals and performance groups, leading to the possibility that online physiology can develop into training or early warning systems.
