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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.
Summary
Physiological signals like electrocardiograms (EKG) and electroencephalograms (EEG) can detect errors and classify performance in robot-assisted surgery simulations, paving the way for new training tools.
Area of Science:
- Robotics in Medicine
- Surgical Simulation Technology
- Physiological Monitoring
Background:
- Operator physiology during robot-assisted surgery simulations is not well understood.
- The potential for physiological signals to identify surgical errors and performance levels requires investigation.
Purpose of the Study:
- To evaluate the measurement of operator physiology during robot-assisted surgery simulations.
- To determine if physiological signals can identify errors and classify high and low performers.
Main Methods:
- Fifty-seven participants engaged in digital simulations using the da Vinci Xi system.
- Analysis involved simulation videos, electrocardiogram (EKG), and electroencephalography (EEG) data.
- Linear mixed-effects models were employed for statistical analysis.
Main Results:
- Errors correlated with significant differences in EKG and EEG measures (e.g., high-frequency power, interbeat interval, theta-to-alpha EEG power ratio).
- Distinct physiological differences were observed between high and low performers.
- Classification models achieved high accuracy in detecting errors (85.7%) and performance groups (96.3%), and predicting upcoming errors (85.7%).
Conclusions:
- Noninvasive physiological recordings effectively differentiate between error and non-error intervals.
- Physiological data can distinguish between different performance groups in surgical simulations.
- Online physiological monitoring offers potential for developing advanced surgical training and early warning systems.
