ArticleJournal of healthcare engineering2022
Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Evaluation of Neutrophil-to-Lymphocyte Ratio and CRP-to-Albumin Ratio in the Risk Stratification of Diabetic Foot Infection Severity.Medicina (Kaunas, Lithuania) · 2026Article
- Machine learning in diabetic-foot osteomyelitis: current applications, challenges, and future directions.Journal of bone and joint infection · 2026Review
- Clinical applications of machine learning for infection assessment in diabetic foot ulcers.Frontiers in physiology · 2026Review
- Advances in Image-Based Diagnosis of Diabetic Foot Ulcers Using Deep Learning and Machine Learning: A Systematic Review.Biomedicines · 2025Review
- Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation, and Detection.Healthcare (Basel, Switzerland) · 2025Review
- Construction and validation of a deep learning-based diagnostic model for segmentation and classification of diabetic foot.Frontiers in endocrinology · 2025Article
- Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.Biomedical engineering online · 2024Article
- Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
- Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.Biosensors · 2022Review
- A feasibility study on the efficacy of a patient-owned wound surveillance system for diabetic foot ulcer care (ePOWS study).Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI.
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Registered trials
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