ArticlePloS one2022
Machine learning for the prediction of minor amputation in University of Texas grade 3 diabetic foot ulcers.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 6 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Research progress on risk prediction models for the diabetic foot.Acta diabetologica · 2025Pooled it
- Diabetic Foot Ulcer Classification Models Using Artificial Intelligence and Machine Learning Techniques: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Frontiers in public health · 2025Pooled it
- The incidence of lower extremity amputation and its associated risk factors in patients with diabetic foot ulcers: A meta-analysis.International wound journal · 2024Pooled it
- Effect of diabetic foot ulcers and other risk factors on the prevalence of lower extremity amputation: A meta-analysis.International wound journal · 2023Pooled it
- Pooled it
- Advances in the application of artificial intelligence-driven multi-modal imaging technologies in the comprehensive diagnosis and treatment of diabetic foot ulcers.Reviews in endocrine & metabolic disorders · 2026Review
- AI-Based Models for Diabetic Foot Ulcer Assessment: Scoping Review.JMIR diabetes · 2026Review
- Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.BioData mining · 2026Article
- Precision nutrition in diabetic foot ulcers: multimodal artificial intelligence for personalized metabolic management.Frontiers in nutrition · 2026Review
- Machine learning-based prediction of major amputation risk after initial limb-preserving surgery in diabetic foot.Frontiers in endocrinology · 2026Article
- Development and External Validation of a Machine Learning Model for Predicting Wound Infection in Diabetic Foot Ulcers.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Interpretable machine learning for predicting major amputation risk in hospitalized diabetic foot ulcer patients: a single-center study with temporal external validation.Frontiers in endocrinology · 2026Article
- Machine learning applications in forecasting patient satisfaction and clinical outcomes after carpal tunnel release: a retrospective study.BMC musculoskeletal disorders · 2025Article
- Predicting major amputation risk in diabetic foot ulcers using comparative machine learning models for enhanced clinical decision-making.Scientific reports · 2025Article
- Predictive model and risk analysis for outcomes in diabetic foot ulcer using eXtreme Gradient Boosting algorithm and SHapley Additive exPlanation.World journal of diabetes · 2025Article
- Machine Learning Insights Into Amputation Risk: Evaluating Wound Classification Systems in Diabetic Foot Ulcers.International wound journal · 2025Article
- Artificial Intelligence as a Tool for Self-Care in Patients with Type 1 and Type 2 Diabetes-An Integrative Literature Review.Healthcare (Basel, Switzerland) · 2025Review
- Article
- An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Minor amputations are performed in a large proportion of patients with diabetic foot ulcers (DFU) and early identification of the outcome of minor amputations facilitates medical decision-making and ultimately reduces major amputations and deaths. However, there are currently no clinical predictive tools for minor amputations in patients with DFU. We aim to establish a predictive model based on machine learning to quickly identify patients requiring minor amputation among newly admitted patients with DFU. Overall, 362 cases with University of Texas grade (UT) 3 DFU were screened from tertiary care hospitals in East China. We utilized the synthetic minority oversampling strategy to compensate for the disparity in the initial dataset. A univariable analysis revealed nine variables to be included in the model: random blood glucose, years with diabetes, cardiovascular diseases, peripheral arterial diseases, DFU history, smoking history, albumin, creatinine, and C-reactive protein. Then, risk prediction models based on five machine learning algorithms: decision tree, random forest, logistic regression, support vector machine, and extreme gradient boosting (XGBoost) were independently developed with these variables. After evaluation, XGBoost earned the highest score (accuracy 0.814, precision 0.846, recall 0.767, F1-score 0.805, and AUC 0.881). For convenience, a web-based calculator based on our data and the XGBoost algorithm was established (https://dfuprediction.azurewebsites.net/). These findings imply that XGBoost can be used to develop a reliable prediction model for minor amputations in patients with UT3 DFU, and that our online calculator will make it easier for clinicians to assess the risk of minor amputations and make proactive decisions.
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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.