ArticleFrontiers in medicine2020
Toward Machine-Learning-Based Decision Support in Diabetes Care: A Risk Stratification Study on Diabetic Foot Ulcer and Amputation.
Article in Frontiers in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 4 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 4 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Research progress on risk prediction models for the diabetic foot.Acta diabetologica · 2025Pooled it
- Risk Factors for First-Ever Diabetes-Related Foot Ulcer: A Systematic Review and Meta-Analysis.International wound journal · 2025Pooled it
- Artificial Intelligence Methods for Diagnostic and Decision-Making Assistance in Chronic Wounds: A Systematic Review.Journal of medical systems · 2025Pooled it
- Pooled it
- Impact of diabetic foot ulcers on mortality risk: Lessons from a 35-year Brazilian cohort followed at an association of people living with diabetes.Archives of endocrinology and metabolism · 2026Article
- Prediction of Diabetes Among Homeless Adults Using Artificial Intelligence: Suggested Recommendations.Healthcare (Basel, Switzerland) · 2026Article
- Observational
- Metabolomic risk predictors of diabetic foot complications: A longitudinal observational study in type 1 diabetes.Diabetes, obesity & metabolism · 2025Observational
- Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study.JMIR diabetes · 2025Article
- Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection.Healthcare (Basel, Switzerland) · 2025Review
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
- Clinical decision support systems for diabetic foot ulcers: a scoping review.Revista da Escola de Enfermagem da U S P · 2024Article
- Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Neural network-based method to stratify people at risk for developing diabetic foot: A support system for health professionals.PloS one · 2023Article
- Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.Biosensors · 2022Review
- Article
- Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning Models for Predicting the Risk of Hard-to-Heal Diabetic Foot Ulcers in a Chinese Population.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers.PloS one · 2022Article
- Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques.Journal of clinical medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes mellitus is associated with serious complications, with foot ulcers and amputation of limbs among the most debilitating consequences of late diagnosis and treatment of foot ulcers. Thus, prediction and on-time treatment of diabetic foot ulcers (DFU) are of great importance for improving and maintaining patients' quality of life and avoiding the consequent socio-economical burden of amputation. In this study, we use Danish national registry data to understand the risk factors of developing diabetic foot ulcers and amputation among patients with diabetes. We analyze the data of 246,705 patients with diabetes to assess some of the main risk factors for developing DFU/amputation. We study the socioeconomic information and past medical history of the patients. Factors, such as low family disposable income, cardiovascular disorders, peripheral artery, neuropathy, and chronic renal complications are among the important risk factors. Mental disorders and depression, albeit not as pronounced, still pose higher risks in comparison to the group of people without these complications. We further use machine learning techniques to assess the practical usefulness of such risk factors for predicting foot ulcers and amputation. Finally, we outline the limitations of working with registry data sources and explain potentials for combining additional public and private data sources in future applications of artificial intelligence (AI) to improve the prediction of diabetic foot ulcers and amputation.
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Registered trials
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.