Evidence map›Paper›PMID 33823845›Full record

ArticleBMC medical education2021

Application of 3D modeling and fusion technology of medical image data in image teaching.

Quan Yuan, Xiaomei Chen, Jian Zhai, Yadi Chen, Qingxiang Liu, Zhongxiao Tan, Gao Chen, Kangle Zhuang, Jianying Zhang, Xi Xu and 2 more

Abstract read
In one paragraph

Article in BMC medical education, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Quan YuanDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Xiaomei ChenDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Jian ZhaiDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Yadi ChenDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Qingxiang LiuDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Zhongxiao TanDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Gao ChenHewanlan digital S T CO.LTD, Shuihu Road on Room204 First building, HeFei, 230000, Anhui, China.
Kangle ZhuangHewanlan digital S T CO.LTD, Shuihu Road on Room204 First building, HeFei, 230000, Anhui, China.
Jianying ZhangDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Xi XuHewanlan digital S T CO.LTD, Shuihu Road on Room204 First building, HeFei, 230000, Anhui, China.
Di QiangDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China.
Xuefei ShaoDepartment of Imaging, Yi-Ji Shan Hospital, Wannan Medical College, Zheshan West Road on the 2nd, Wuhu, 241000, Anhui, China. drshao@163.com.

Funding

Anhui Provincial Key Teaching Research Project Grant No. 2019jyxm1058Fundação de Apoio ao Ensino, Pesquisa e Extensão de Itajubá 2018jyxm58 and 2020jyxm26 and 2020jyxm16
6 · The paper itself

Abstract

backgroundWe combined anatomy with imaging, transformed the 2D information of various imaging techniques into 3D information, and form the assessment system of real medical imaging cases in order to make up for the deficiencies in the current teaching of the medical imaging technology students.

methodsA total of 460 medical imaging students were selected and randomly divided into two groups. The research group received the teaching of the fusion of the original CT and MR data 3D model and the original image combined with 3D anatomical image. CT and MRI data are imported through load DICOM of 3D slicer. Different tissues and organs are segmented by threshold and watershed algorithm of segment editor module. Models are exported through export / import models and label maps in segmentation. Save the NHDR file of the original data and Obj file of the corresponding model through save the NHDR and corresponding Obj files are loaded into probe 1.0 software. The software can give different colors to the three-dimensional models of different organs or tissues to display the stereo models and related data, and display the hook edges of organ models on coronal, sagittal and axial images. At the same time, annotation can be established in the corresponding anatomical position. Finally, it can be saved as a single file of Hwl, and the teaching can be opened at any time through the program of probe 1.0. Statistical analysis Academic self-efficacy scale and Self-directed learning ability scale was adopted by self-directed learning evaluation scale between two groups.

resultsCompare the theoretical scores and case analysis scores of the two groups. The scores of the study and control groups were significantly higher than those of the control group. Before the experiment, no significant difference was detected in the self-efficacy of learning ability and learning behavior between the two groups, while after the experiment, these differences between the two groups were statistically significan. Moreover, the learning ability self-efficacy and learning behavior of the two groups of students after the experiment was significantly higher than that before the experiment. The self-efficacy of the learning behavior of the control group was higher after the experiment than that before the experiment, albeit the difference was not statistically significant.

conclusionsThe modern, information-based and humanized experimental teaching mode will be constantly improved under the support of PACS system in order to optimize the medical imaging teaching activities for the development of modern medical education.

Indexed as

Imaging, Three-DimensionalStudents, MedicalHumansLearningMagnetic Resonance ImagingTeachingTechnology3D modelingMedical image;image teachingPACS

Identifiers

PMID33823845
PMCPMC8025386

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.