Department of Surgery (Otolaryngology), University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Vic., Australia.
Clin Otolaryngol. 2021 Sep;46(5):961-968. doi: 10.1111/coa.13760. Epub 2021 Mar 29.
Cortical mastoidectomy is a core skill that Otolaryngology trainees must gain competency in. Automated competency assessments have the potential to reduce assessment subjectivity and bias, as well as reducing the workload for surgical trainers.
This study aimed to develop and validate an automated competency assessment system for cortical mastoidectomy.
Data from 60 participants (Group 1) were used to develop and validate an automated competency assessment system for cortical mastoidectomy. Data from 14 other participants (Group 2) were used to test the generalisability of the automated assessment.
Participants drilled cortical mastoidectomies on a virtual reality temporal bone simulator. Procedures were graded by a blinded expert using the previously validated Melbourne Mastoidectomy Scale: a different expert assessed procedures by Groups 1 and 2. Using data from Group 1, simulator metrics were developed to map directly to the individual items of this scale. Metric value thresholds were calculated by comparing automated simulator metric values to expert scores. Binary scores per item were allocated using these thresholds. Validation was performed using random sub-sampling. The generalisability of the method was investigated by performing the automated assessment on mastoidectomies performed by Group 2, and correlating these with scores of a second blinded expert.
The automated binary score compared with the expert score per item had an accuracy, sensitivity and specificity of 0.9450, 0.9547 and 0.9343, respectively, for Group 1; and 0.8614, 0.8579 and 0.8654, respectively, for Group 2. There was a strong correlation between the total scores per participant assigned by the expert and calculated by the automatic assessment method for both Group 1 (r = .9144, P < .0001) and Group 2 (r = .7224, P < .0001).
This study outlines a virtual reality-based method of automated assessment of competency in cortical mastoidectomy, which proved comparable to the assessment provided by human experts.
颅中窝乳突切除术是耳鼻喉科受训者必须掌握的核心技能。自动化能力评估有可能减少评估的主观性和偏见,同时减少外科培训师的工作量。
本研究旨在开发和验证颅中窝乳突切除术的自动化能力评估系统。
来自 60 名参与者(第 1 组)的数据用于开发和验证颅中窝乳突切除术的自动化能力评估系统。来自另外 14 名参与者(第 2 组)的数据用于测试自动化评估的泛化能力。
参与者在虚拟现实颞骨模拟器上钻颅中窝乳突切除术。程序由一位盲目的专家使用先前验证过的墨尔本乳突切除术量表进行评分:第 1 组和第 2 组由不同的专家进行评估。使用第 1 组的数据,开发了模拟器指标,以便直接映射到该量表的各个项目。通过将自动模拟器指标值与专家评分进行比较,计算出指标值阈值。使用这些阈值分配每个项目的二进制分数。通过随机子抽样进行验证。通过对第 2 组进行的乳突切除术进行自动评估,并与第二位盲目的专家的评分进行相关分析,研究了该方法的通用性。
第 1 组的自动化二进制评分与每项专家评分的准确性、敏感性和特异性分别为 0.9450、0.9547 和 0.9343;第 2 组的分别为 0.8614、0.8579 和 0.8654。第 1 组(r=.9144,P<.0001)和第 2 组(r=.7224,P<.0001)中,每位参与者的专家评分和自动评估方法计算的总分之间存在很强的相关性。
本研究概述了一种基于虚拟现实的颅中窝乳突切除术能力自动评估方法,该方法与人类专家提供的评估相当。