Evidence map›Paper›PMID 41678459›Full record

ArticlePloS one2026

An online training platform for SPECT imaging technology utilizing three-dimensional modeling.

Lihua Qiao, Hongzhi Wang, Xiaorui Guo, Xinkun Lei, Hua Shang, Ruibin Zhao, Tian Xia, Ruiping Qin, Zikun Fang, Luqi Shou and 2 more

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Lihua QiaoSchool of Medical Imaging, Hebei Medical University, Shijiazhuang, Hebei, China.ORCID https://orcid.org/0000-0002-8768-5342
Hongzhi WangShanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
Xiaorui GuoDepartment of Nuclear Medicine, the Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Xinkun LeiDepartment of Nuclear Medicine, the Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Hua ShangDepartment of Nuclear Medicine, the Second Affiliated Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Ruibin ZhaoSchool of Medical Imaging, Hebei Medical University, Shijiazhuang, Hebei, China.
Tian XiaShanghai Peiyun Education Technology Company Limited, Shanghai, China.
Ruiping QinSchool of Medical Imaging, Hebei Medical University, Shijiazhuang, Hebei, China.
Zikun FangSchool of Medical Imaging, Hebei Medical University, Shijiazhuang, Hebei, China.
Luqi ShouSchool of Medical Imaging, Hebei Medical University, Shijiazhuang, Hebei, China.
Yiwen QinSchool of Medical Imaging, Hebei Medical University, Shijiazhuang, Hebei, China.
Dandan ShangBiochemistry and molecular biology, Hebei Medical University, Shijiazhuang, Hebei, China.ORCID https://orcid.org/0000-0002-1038-6598

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe provision of nuclear medicine experimental classes within universities poses significant challenges due to the high risk, substantial cost, and large size of the requisite equipment. To address the bespoke training needs of students majoring in imaging technology in this new medical era, our team has developed an online training platform specifically for SPECT imaging technology, a key aspect of nuclear medicine.

methodThis platform utilises advanced technologies such as Unity3D to create six three-dimensional scene modules of SPECT imaging and seven typical disease examination operation processes. As a result, we have achieved a human-computer interactive three-dimensional virtual system. The aforementioned teaching strategy has been implemented in our institution's instructional practice across five semesters, in addition to being adopted by three other medical colleges.

resultsTraining data reveals that the overall sample score distribution aligns with a normal distribution, suggesting that the platform's structure is logically and effectively designed. Furthermore, the linear fit slopes of individual sample scores are consistently positive, indicating that the frequency of training sessions yields a positive feedback effect on students' bespoke training. The innovative nature of this platform is protected through computer software copyrights.

conclusionOur online training platform enhances course structure and student training objectives, effectively accommodating the requirements of nuclear medicine teaching for personalized student training, innovative thinking, and the "early clinical" mindset.

Indexed as

Imaging, Three-DimensionalNuclear MedicineTomography, Emission-Computed, Single-PhotonHumansUser-Computer Interface

Identifiers

PMID41678459
PMCPMC12900319

What OpenQuestion holds

Textmetadata
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.