Physiological Detection of Intraoperative Errors During Robot-Assisted Surgery

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-07-09 DOI:10.1002/rcs.70090

Christopher D'Ambrosia, Estella Y. Huang, Nicole H. Goldhaber, Henrik Christensen, Ryan C. Broderick, Lawrence G. Appelbaum

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引用次数: 0

Abstract

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.

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机器人辅助手术中术中错误的生理检测

本研究在机器人辅助手术模拟中测试了操作人员在操作过程中的生理测量，以确定这些信号是否可以识别错误并对高绩效和低绩效进行分类。方法57名受试者在达芬奇Xi系统上进行数字模拟。采用线性混合效应模型分析模拟视频、心电图（EKG）和脑电图（EEG）。结果与非误差时段相比，误差时段引起的心电图和脑电图测量值（包括高频功率、搏动间隔和θ - α脑电图功率比）存在显著差异。在这些测量中，高绩效和低绩效者在几个方面存在显著差异，而分类模型对于错误检测（85.7%）和绩效组（96.3%）的准确率较高，并且使用导致错误的生理信号可以准确预测即将发生的错误（85.7%）。结论无创生理记录可以区分误差、非误差区间和性能组，为在线生理发展为训练或预警系统提供了可能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.