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VR/AR Technology in Human Anatomy Teaching and Operation Training
Author(s) -
Yuan Zhou,
Jiejun Hou,
Qi Liu,
Xu Chao,
Nan Wang,
Yu Chen,
Jianjun Guan,
Qi Zhang,
Yongchang Diwu
Publication year - 2021
Publication title -
journal of healthcare engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.509
H-Index - 29
eISSN - 2040-2309
pISSN - 2040-2295
DOI - 10.1155/2021/9998427
Subject(s) - computer science , feature (linguistics) , segmentation , virtual reality , wired glove , artificial intelligence , deep learning , training system , interface (matter) , human–computer interaction , philosophy , linguistics , bubble , maximum bubble pressure method , parallel computing , economics , economic growth
AR/VR technology can fuse the clinical imaging data and information to build an anatomical environment combining virtual and real, which is helpful to improve the interest of teaching and the learning initiative of medical students, and then improve the effect of clinical teaching. This paper studies the application and learning effect of the VR/AR system in human anatomy surgery teaching. This paper first shows the learning environment and platform of the VR/AR system, then explains the interface and operation of the system, and evaluates the teaching situation. This paper takes the VR/AR operation simulation system of an Irish company as an example and evaluates the learning effect of 41 students in our hospital. Research shows that the introduction of the feature reweighting module in the VR/AR surgery simulation system improves the accuracy of bone structure segmentation (IOU value increases from 79.62% to 83.56%). For real human ultrasound image data, the IOU value increases from 80.21% to 82.23% after the feature reweighting module is introduced. Therefore, the dense convolution module and feature reweighting module improve the learning ability of the network for bone structure features in ultrasound images from two aspects of feature connection and importance understanding and effectively improve the performance of bone structure segmentation.

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