Evidence map›Paper›PMID 42210312›Full record

ArticleBMC medical education2026

Benefit of mixed reality assistance on CT-based ear anatomy spatial recognition: a preliminary study.

Jia Guo, Jianguo Duan, Yandi He, Huilin Cui, Chengyu Zhang, Zhen Yu, Yuqi Jiang, Qinglei Zhang, Haoyue Tan, Huan Jia

Abstract read
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Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Jia Guo *Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jianguo Duan *Shanghai Maritime University, Shanghai, China.
Yandi HeEar Institute, Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Huilin CuiEar Institute, Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Chengyu ZhangShanghai Jiao Tong University School of Medicine, Shanghai, China.
Zhen YuShanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuqi JiangEar Institute, Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qinglei ZhangShanghai Maritime University, Shanghai, China. qlzhang@shmtu.edu.cn.
Haoyue TanEar Institute, Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 120050@sh9hospital.org.cn.
Huan JiaEar Institute, Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China. huan.jia.orl@shsmu.edu.cn.

Funding

Clinical Research Plan of SHDC SHDC2020CR3010ANational Key Research and Development Program 2023YFB4705805, 2023YFB4705800Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases 14DZ2260300the 17th College Students' Innovation and Entrepreneurship Training Program of Shanghai Jiao Tong University School of Medicine 1723X512
6 · The paper itself

Abstract

backgroundTo evaluate the benefit of a mixed reality (MR) platform on CT-based ear anatomy spatial recognition and annotation performance by comparing with traditional learning.

methodsA prospective study (N=8) assigned undergraduate medical students to the MR group and traditional group (textbooks and images). Each completed identification exercises of 8 key anatomical structures on 20 temporal bone CT cases over three learning phases. Outcomes included annotation accuracy, task completion time and learning curve. Participants completed a Likert-style questionnaire evaluating the learning experience and the adverse health effects experienced.

resultsThe MR group achieved an earlier learning plateau and better performance than the traditional group (6.0±3.5 vs. 8.0±4.9 cases). Mixed-design ANOVA demonstrated consistent, large-magnitude performance advantages for the MR group in annotation accuracy (η²ₚ=0.605) and task completion time (η²ₚ=0.875). MR teaching method was most advantageous for complex structures such as the ossicular chain and facial nerve. Learning experience scores were higher with MR, though 50% of MR users reported mild visual fatigue.

conclusionsIn this preliminary study, MR-assisted learning shortened the learning curve and improved performance on a CT-based temporal bone annotation task compared with textbook-based learning, with the most pronounced advantages observed for spatially complex structures. These findings indicate the potential of MR as an adjunct in CT-based ear anatomy training and warrant evaluation in larger cohorts.

Indexed as

AnatomyEarEducation, Medical, UndergraduateTomography, X-Ray ComputedVirtual RealityFemaleHumansLearning CurveMaleProspective StudiesTemporal BoneAnatomy educationEarHoloLensLearning curveMixed realityTemporal bone

Identifiers

PMID42210312
PMCPMC13411741

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.