Maini Arjun, Pisa Justyn, Davari Mina, Unger Bert, Hochman Jordan
University of Manitoba, Max Rady College of Medicine Winnipeg Manitoba Canada.
Department of Biomedical Engineering University of Manitoba Winnipeg Manitoba Canada.
Laryngoscope Investig Otolaryngol. 2025 Aug 20;10(4):e70188. doi: 10.1002/lio2.70188. eCollection 2025 Aug.
Simulation-based surgical training is now standard in residency education - aided by tools such as printed, virtual, and augmented reality environments. Autonomous education with use of machine learning is an emerging necessity owing to resident work-hour limitations and staff availability. An essential first step to providing automated feedback during simulated surgery is the development of a tool to classify surgical technique. Distinctive hand motion and drilling patterns can be used in the assessment of trainee proficiency during complex temporal bone surgery (TBS).This article reviews the development of a software classifier model for automated assessment of surgical performance based on recorded drill trajectory and hand motion tracking during 3D-printed TBS.
REB-approved prospective experimental study, in which a classifier was developed to provide automatic assessment of surgical performance based on drill trajectory and hand motion tracking. Four expert (two otologic surgeons and two PGY5 surgery residents) and four novice (PGY1-3 surgery residents) participants dissected 3D-printed temporal bone models. Individual hand and drill motion data were collected and analyzed for similarities and variations between participants to develop a model to predict the level of expertise (expert or novice), using a supervised classification approach.
The automated stroke detection algorithm found 80.2%, 82.7%, and 84.8% precision in stroke detection and classification during cortical mastoidectomy (CM), thinning procedures (TP) and facial recess exposure (FRE), respectively. The classifier was able to predict the level of expertise with an accuracy of 92.8% and a sensitivity of 87.5%.
A temporal bone classifier can be developed with a high degree of accuracy as an initial stage towards an autonomous training paradigm.
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基于模拟的外科培训现已成为住院医师教育的标准——借助印刷、虚拟和增强现实环境等工具。由于住院医师工作时间限制和工作人员可用性，利用机器学习进行自主教育已成为一种新的必要需求。在模拟手术期间提供自动反馈的关键第一步是开发一种用于对外科技术进行分类的工具。独特的手部动作和钻孔模式可用于评估复杂颞骨手术（TBS）期间学员的熟练程度。本文回顾了一种软件分类器模型的开发，该模型基于3D打印TBS期间记录的钻孔轨迹和手部动作跟踪对手术表现进行自动评估。
经REB批准的前瞻性实验研究，其中开发了一种分类器，以基于钻孔轨迹和手部动作跟踪对手术表现进行自动评估。四名专家（两名耳科外科医生和两名PGY5外科住院医师）和四名新手（PGY1 - 3外科住院医师）参与解剖3D打印的颞骨模型。收集并分析个体手部和钻孔动作数据，以了解参与者之间的异同，采用监督分类方法开发一个模型来预测专业水平（专家或新手）。
自动笔画检测算法在皮质乳突切除术（CM）、减薄手术（TP）和面神经隐窝暴露（FRE）期间的笔画检测和分类中，准确率分别为80.2%、82.7%和84.8%。该分类器能够以92.8%的准确率和87.5%的灵敏度预测专业水平。
可以开发出具有高度准确性的颞骨分类器，作为迈向自主训练模式的初始阶段。
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