Evidence map›Paper›PMID 40258927›Full record

ArticleScientific reports2025

A novel deep learning approach to classify 3D foot types of diabetic patients.

Pui-Ling Li, Qin-Feng Xiao, Kit-Lun Yick, Qi-Long Liu, Li-Ying Zhang

Abstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Pui-Ling LiSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China.
Qin-Feng XiaoSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China.
Kit-Lun YickSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China. tcyick@polyu.edu.hk.
Qi-Long LiuSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China.
Li-Ying ZhangSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes mellitus is a worldwide epidemic that leads to significant changes in foot shape, deformities, and ulcers. Precise classification of diabetic foot not only helps identify foot abnormalities but also facilitates personalized treatment and preventive measures through the engineering design of foot orthoses. In this study, we propose a novel deep learning method based on DiffusionNet which incorporates a self-attention mechanism and external features to classify the foot types of diabetic patients into six categories by using simple 3D foot images directly. Our approach achieves a high accuracy of 82.9% surpassing existing machine and deep learning methods. The proposed model offers a cost-effective way to analyse foot shapes and facilitate the customization process for both the footwear industry and medical applications.

Indexed as

Deep LearningDiabetic FootFootImaging, Three-DimensionalHumansDiabetic footDiffusionNetFoot type classificationMulti-layer perceptron (MLP)Self-attention mechanismVisual computing

Identifiers

PMID40258927
PMCPMC12012058

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.