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Physiological correlates of cognitive load in laparoscopic surgery
Zohreh Zakeri1, Neil Mansfield2, Caroline Sunderland3
1Department of Engineering, School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. zohreh.zakeri@ntu.ac.uk.
Scientific Reports
|August 2, 2020
Summary
Physiological data from cardiac and ocular variables can accurately assess surgeon cognitive load during laparoscopic surgery. This technology can improve surgical training and certification by providing objective performance metrics.
Area of Science:
- Medical Engineering
- Surgical Training
- Human Factors Engineering
Background:
- Laparoscopic surgery imposes significant cognitive load on surgeons, impacting patient safety and healthcare costs.
- Cognitive load diminishes with surgical proficiency, highlighting the need for objective assessment methods.
- Current training and certification rely on subjective and behavioral measures, which may not fully capture cognitive demands.
Purpose of the Study:
- To investigate the utility of physiological data (cardiac and ocular variables) for assessing cognitive load in novice laparoscopic surgeons.
- To correlate physiological measures with traditional behavioral and subjective assessments of performance and difficulty.
- To evaluate the predictive power of multimodal physiological features using different analytical models.
Main Methods:
- Collected cardiac and ocular physiological data from 31 novice surgeons during laparoscopic exercises.
- Utilized a dual-task paradigm to manipulate and measure cognitive load.
- Compared physiological features against traditional measures like reaction time, completion time, and subjective difficulty ratings.
- Employed artificial neural networks and linear regression for data analysis.
Main Results:
- Physiological features significantly correlated with traditional measures of task difficulty, reaction time, and completion time.
- Heart rate and heart rate variability predicted reaction times to auditory stimuli.
- Completion times were accurately predicted by physiological measures (correlation coefficient = 0.84).
- Multimodal physiological features outperformed individual features, and artificial neural networks surpassed linear regression in predictive accuracy.
Conclusions:
- Physiological monitoring offers a robust, objective method for assessing cognitive load in laparoscopic surgery.
- These findings support the development of technology-driven, standardized frameworks for surgical training and certification.
- Objective assessment of cognitive load can enhance patient safety and optimize surgical education programs.
