Evidence map›Paper›PMID 40619531›Full record

ArticleScientific reports2025

Improving lesion detection skills in medical imaging education through enhanced peripheral visual perception.

Fenghong Wang, Guoye Liu, Siu Shing Man

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

3 authors.

Fenghong WangSchool of Design, South China University of Technology, Guangzhou, China.
Guoye LiuSchool of Design, South China University of Technology, Guangzhou, China.
Siu Shing ManSchool of Design, South China University of Technology, Guangzhou, China. ssman6@scut.edu.cn.

Funding

Guangzhou Municipal Science and Technology Bureau 2024A04J2279National Natural Science Foundation of China 72301110
6 · The paper itself

Abstract

Accurate medical image diagnosis is essential in clinical practice and places high demands on the diagnostic skills of imaging professionals. However, a significant shortage of radiologists highlights the gap between supply and demand, leading to the importance of education. Traditional education has largely neglected the development of visual perception skills, which play a critical role in lesion detection. This study aims to investigate visual training methods that can effectively enhance learners' perceptual abilities in medical image interpretation through a pre-post experiment. In the experiment, the participants of intervention groups underwent intensive training targeting uncovered visual areas under central or peripheral vision occlusion conditions. The result showed that peripheral vision training significantly improved participants' diagnostic mean accuracy (from 59.5 to 68.0%, p < 0.001), mean sensitivity (from 69.0 to 79.5%, p < 0.001), mean positive predictive value (from 65.6 to 82.9%, p < 0.001), and reduced mean task time (from 1163.4s to 877.3s, p < 0.001). However, neither peripheral nor central vision training significantly improved specificity and negative predictive value, which were metrics of negative diagnostic ability. Consequently, enhancing peripheral vision perception helped to reduce missed diagnoses, but with limited impact on reducing misdiagnosis. The peripheral training enhanced the effectiveness of medical image diagnosis education to provide competent professionals in the field.

Indexed as

Diagnostic ImagingEducation, MedicalVisual PerceptionAdultClinical CompetenceFemaleHumansMaleEducationLesion detectionMedical imagePeripheral visionVisual training

Identifiers

PMID40619531
PMCPMC12230107

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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.