Evidence map›Paper›PMID 34211684›Full record

ArticleJournal of healthcare engineering2021

VR/AR Technology in Human Anatomy Teaching and Operation Training.

Yuan Zhou, Jiejun Hou, Qi Liu, Xu Chao, Nan Wang, Yu Chen, Jianjun Guan, Qi Zhang, Yongchang Diwu

RetractedAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Yuan ZhouBasic Medical College, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.ORCID 0000-0003-1322-4970
Jiejun HouAffiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Qi LiuDiscipline Innovation Team of Shaanxi University of Chinese Medicine, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Xu ChaoBasic Medical College, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Nan WangBasic Medical College, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Yu ChenBasic Medical College, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Jianjun GuanBasic Medical College, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Qi ZhangShaanxi Key Laboratory of Chinese Medicine Encephalopathy, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.
Yongchang DiwuShaanxi Key Laboratory of Chinese Medicine Encephalopathy, Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi, China.ORCID 0000-0001-7868-042X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Virtual RealityComputer SimulationHumansLearningTechnology

Identifiers

PMID34211684
PMCPMC8205599

What OpenQuestion holds

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Registered trials

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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.