Sahin S. S., Zengin C., Keleş H. O.
ARTIFICIAL INTELLIGENCE SURGERY, vol.6, no.1, pp.171-187, 2026 (ESCI, Scopus)
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Publication Type:
Article / Article
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Volume:
6
Issue:
1
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Publication Date:
2026
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Doi Number:
10.20517/ais.2025.109
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Journal Name:
ARTIFICIAL INTELLIGENCE SURGERY
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Journal Indexes:
Scopus, Emerging Sources Citation Index (ESCI)
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Page Numbers:
pp.171-187
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Ankara University Affiliated:
Yes
Abstract
Aim: Laparoscopic skill assessment traditionally relies on subjective evaluation, which lacks objectivity and consistency. Automated multimodal approaches integrating tool-pressure and neural data may improve the reliability and scalability of skill assessment. Therefore, our objectives were to: (1) integrate a pressure-sensing unit into box-trainer simulators and laparoscopic tools to investigate tool-pressure features as objective indicators of surgical skill; and (2) combine electroencephalography (EEG)-derived power spectral density (PSD) and phase-locking value (PLV) features with tool-pressure data to evaluate the classification performance of different machine learning models.
Methods: Tool-pressure, EEG, and ECG data, along with task completion time, error counts, and National Aeronautics and Space Administration Task Load Index (NASA-TLX) workload scores, were collected from 10 surgeons and 13 inexperienced students performing a peg-transfer laparoscopic task. Pressure sensors were integrated into the right and left laparoscopic graspers. EEG features were extracted from four frequency bands using PSD and PLV. Three machine learning models - random forest classifier (RFC), Gaussian process classifier (GPC), and AdaBoost classifier (ABC) - were used to classify participants into surgeon and inexperienced groups.
Results: Right-left pressure asymmetry emerged as a reliable indicator of surgical expertise compared with other pressure metrics. Using only this feature, RFC achieved up to 78% classification accuracy. The highest performance occurred when combining theta-band power features with pressure asymmetry, where RFC and ABC reached 86% accuracy [F1 score = 0.83; area under the curve (AUC) = 0.92 for RFC].
Conclusion: This multimodal approach combining psychomotor and neurophysiological measures enhances the objectivity of surgical skill evaluation and may support real-time feedback systems for laparoscopic training.