Evidence map›Paper›PMID 42460325›Full record

Observational studyFrontiers in endocrinology2026

Deep learning-derived retinal biomarker associated with diabetes-related amputation in type 2 diabetes.

Junseok Park, Jung Soo Yoon, Sahil Thakur, Dongjin Nam, Sunjin Hwang

Abstract readObservational Study
In one paragraph

Observational study in Frontiers in endocrinology, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Junseok ParkSeoul National University Hospital, Seoul, Republic of Korea.
Jung Soo YoonDepartment of Plastic and Reconstructive Surgery, Myongji Medical Foundation, Myongji Hospital, Ilsan, Republic of Korea.
Sahil ThakurMediwhale Inc., Seoul, Republic of Korea.
Dongjin NamMediwhale Inc., Seoul, Republic of Korea.
Sunjin HwangDepartment of Ophthalmology, Hanyang University Guri Hospital, Guri, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic foot (DF)-related amputation remains a major cause of morbidity in patients with type 2 diabetes, yet biomarkers associated with amputation risk remain limited. We aimed to assess the independent association between a deep learning (DL)-derived retinal biomarker using coronary artery calcification (Dr. Noon CVD) and DF-related amputation in patients with type 2 diabetes. This retrospective observational study conducted in a university hospital in South Korea included 392 individuals with type 2 diabetes receiving ophthalmic care (79 with DF-related amputation and 313 without). Participants were randomly split into training (70%) and validation (30%) sets. Model performance in relation to DF-related amputation was assessed using area under the receiver operating characteristic curve (AUC), continuous net reclassification index (cNRI), and integrated discrimination improvement (IDI). Prespecified rule-out (sensitivity ≥0.85) and rule-in (specificity ≥0.90) thresholds were also applied. Adding the retinal biomarker to a basic clinical model showed incremental association with DF-related amputation in the validation set (AUC 0.146, 95% CI: 0.046-0.249; cNRI 0.629, 95% CI: 0.184-1.027; IDI 0.062, 95% CI: 0.012-0.110). The full model achieved an AUC of 0.791, and the association remained consistent across sensitivity analyses using DF risk score derived from the external dataset. Under a 27% amputation prevalence (based on the reported prevalence of amputation among patients with diabetic foot), sensitivity was 87.5% with a negative predictive value of 92.6% at the rule-out cutoff; specificity was 90.4% with a positive predictive value of 56.3% at the rule-in cutoff. A DL-derived retinal biomarker shows meaningful association with DF-related amputation beyond conventional diabetes variables.

Indexed as

Amputation, SurgicalBiomarkersDeep LearningDiabetes Mellitus, Type 2Diabetic FootRetinaAgedDiabetic RetinopathyFemaleHumansMaleMiddle AgedRepublic of KoreaRetrospective StudiesBiomarkersamputationartificial intelligencedeep learningdiabetesdiabetic footretina

Identifiers

PMID42460325
PMCPMC13368666

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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.