Evidence map›Paper›PMID 41999359›Full record

ArticleHuman factors2026

AI-Enhanced Modular Material Selection for Patient-Specific Diabetic Insoles via Silicone Molding.

Li-Ying Zhang, Kit-Lun Yick, Joanne Yip, Sun-Pui Ng

Abstract read
In one paragraph

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

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0citing papers in PubMed
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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

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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

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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

4 authors.

Li-Ying ZhangSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China.
Kit-Lun YickSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China.
Joanne YipSchool of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China.
Sun-Pui NgSchool of Professional Education and Executive Development, The Hong Kong Polytechnic University, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ObjectiveThis study proposes an AI-enhanced modular material selection approach for designing diabetic insoles. By customizing materials for forefoot and heel modules, the method enables personalized support and pressure redistribution, resulting in cost-effective insoles.BackgroundDiabetic foot ulcers occur as a result of elevated plantar pressure and poor foot sensation. To mitigate this risk, developing insoles that redistribute plantar pressure can significantly lower the likelihood of ulcer formation.MethodThe insole was fabricated using cost-effective silicone molding, with functional personalization achieved via an AI-enhanced approach that selected optimal, interchangeable cushioning materials for forefoot and heel modules of each patient. The design integrates a ¾-length porous silicone upper layer with regionally optimized materials. Laboratory wear trials involving 23 diabetic patients compared the offloading performance of the hybrid insole (AIO) against barefoot, a PORON® Medical 4708 insole (PUR), and a cork-EVA insole (EVA).ResultsAIO demonstrated a 37.6% reduction in peak plantar pressure compared to barefoot condition while increasing contact area by 36.0% across the plantar surface. It significantly outperformed the commercial insole (EVA) and matched a therapeutic insole (PUR) overall, with superior regional offloading at the heel.ConclusionThis study enhances diabetic foot care by combining efficiency of silicone molding with functionally personalized, modular design to achieve superior pressure redistribution. It establishes a paradigm of "modular personalization" for diabetic insoles, leveraging AI for patient-specific material selection within a standardized, biomechanically optimized geometry.ApplicationThe findings offer practical insights for clinicians and manufacturers seeking scalable, patient-specific solutions for preventing diabetic foot ulcers.

Indexed as

Diabetic FootEquipment DesignFoot OrthosesSiliconesFemaleHumansMaleMiddle AgedSiliconesAI-enhanced material selectiondiabetes mellitusdiabetic insolediabetic ulcerpatient-specific designpressure offloadingsilicone molding

Identifiers

PMID41999359
PMCPMC13526202

What OpenQuestion holds

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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.