ArticleScientific reports2025
Neural insights on expert surgeons' mental workload during live robotic surgeries.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From Clinical Technique to Interprofessional Implementation and Intelligent Integration: A Bibliometric Analysis of the Interdisciplinary Expansion of Robot-Assisted Spine Surgery.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Multimodal physiological correlates of surgeon stress in live robot-assisted surgery.Surgical endoscopy · 2026Observational
- Humanization of Surgical Care in the Robotic Age: A Triangular Interaction Model Between the Surgeon, the Patient, and the Technology.Healthcare (Basel, Switzerland) · 2026Review
- Predicting task performance in robot-assisted surgery using physiological stress and subjective workload: a case study with interpretable machine learning.Frontiers in human neuroscience · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Despite its adoption and benefits, robotic surgeries can impose additional mental workload on surgeons. Validated questionnaires mostly administered at the end of procedures may not accurately capture the dynamic nature of mental workload over an entire procedure. Hence, we sought to determine if electroencephalogram (EEG) based neural activities in different brain regions can measure variations in expert surgeons' mental workload intraoperatively. EEG data was collected from five different surgeons performing 13 robotic-assisted urological procedures. Data analysis focused on three surgery phases (before, critical, and after). After performing each phase, surgeons provided a rating of their perceived mental workload. A linear mixed effects model was applied to explore the impact of the study phases on the relative spectral band power of EEG signals. The relative theta band power in the frontal brain region was highest during the critical portions of the procedure (p < 0.05). As the subjective ratings increased, the relative frontal theta band power increased (p < 0.001) while the relative parietal alpha band power decreased across all phases. We show that EEG signals can distinguish intraoperative workload in robotic surgeries. This has several applications including predicting risk factors for increased case complexity and surgical education.
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Registered trials
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