Classification in Virtual Temporal Bone Surgical Education: A First Step Towards Automated Virtual Education With Use of Machine Learning

IF 1.7 4区医学 Q2 OTORHINOLARYNGOLOGY

Laryngoscope Investigative Otolaryngology Pub Date : 2025-08-20 DOI:10.1002/lio2.70188

Arjun Maini, Justyn Pisa, Mina Davari, Bert Unger, Jordan Hochman

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引用次数: 0

Abstract

Objective

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.

Methods

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.

Results

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%.

Conclusion

A temporal bone classifier can be developed with a high degree of accuracy as an initial stage towards an autonomous training paradigm.

Level of Evidence

IV.

Abstract Image

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虚拟颞骨手术教育的分类：利用机器学习迈向自动化虚拟教育的第一步

目的基于模拟的外科培训现在是住院医师教育的标准，在打印、虚拟和增强现实环境等工具的辅助下。由于居民工作时间的限制和工作人员的可用性，使用机器学习的自主教育是一种新兴的必要性。在模拟手术中提供自动反馈的关键第一步是开发一种分类手术技术的工具。在复杂颞骨手术（TBS）中，独特的手部运动和钻孔模式可用于评估受训者的熟练程度。本文回顾了一种软件分类器模型的发展，该模型基于3d打印TBS期间记录的钻头轨迹和手部运动跟踪，用于自动评估手术性能。方法采用reb批准的前瞻性实验研究，开发了一种基于钻孔轨迹和手部运动跟踪的分类器，用于自动评估手术性能。4名专家（2名耳科医生和2名PGY5外科住院医师）和4名新手（PGY1-3外科住院医师）参与了3d打印颞骨模型的解剖。收集并分析参与者之间的相似性和差异，利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）。结果脑卒中自动检测算法在皮质乳突切除术（CM）、减薄术（TP）和面隐窝暴露术（FRE）中脑卒中检测和分类准确率分别为80.2%、82.7%和84.8%。该分类器预测专业水平的准确率为92.8%，灵敏度为87.5%。结论颞骨分类器可作为自主训练模式的初始阶段，具有较高的准确率。证据级别IV。

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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