ArticleHealth information science and systems2022
Holistic multi-class classification & grading of diabetic foot ulcerations from plantar thermal images using deep learning.
Article in Health information science and systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- 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
- Improving Diabetic Foot Care With Infrared Thermography and Artificial Intelligence: A Review.Journal of diabetes science and technology · 2026Review
- In-Shoe Foot Temperature Patterns During Lying, Sitting and Standing Postures: Baseline Data from Healthy Individuals.Sensors (Basel, Switzerland) · 2026Article
- Use of thermal imaging in the detection of the diabetic foot- review.Reviews in endocrine & metabolic disorders · 2026Review
- Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.Biosensors · 2024Review
- Differential Diagnosis of Diabetic Foot Osteomyelitis and Charcot Neuropathic Osteoarthropathy with Deep Learning Methods.Journal of imaging informatics in medicine · 2024Article
- Construction of a 3-year risk prediction model for developing diabetes in patients with pre-diabetes.Frontiers in endocrinology · 2024Article
- The protective role of oily fish intake against type 2 diabetes: insights from a genetic correlation and Mendelian randomization study.Frontiers in nutrition · 2024Article
- Mobile phone thermography of the toes in patients with systemic sclerosis-a pilot study.Rheumatology advances in practice · 2024Article
- Article
- Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Diabetic wounds and artificial intelligence: A mini-review.World journal of clinical cases · 2023Review
- Serum uric acid and risk of diabetic neuropathy: a genetic correlation and mendelian randomization study.Frontiers in endocrinology · 2023Article
- Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.Biosensors · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Diabetic foot is a common complication associated with diabetes mellitus (DM) leading to ulcerations in the feet. Due to diabetic neuropathy, most patients have reduced sensitivity to pain. As a result, minor injuries go unnoticed and progress into ulcers. The timely detection of potential ulceration points and intervention is crucial in preventing amputation. Changes in plantar temperature are one of the early signs of ulceration. Previous studies have focused on either binary classification or grading of DM severity, but neglect the holistic consideration of the problem. Moreover, multi-class studies exhibit severe performance variations between different classes. Methods: We propose a new convolutional neural network for discrimination between non-DM and five DM severity grades from plantar thermal images and compare its performance against pre-trained networks such as AlexNet and related works. We address the lack of data and imbalanced class distribution, prevalent in prior work, achieving well-balanced classification performance. Results: Our proposed model achieved the best performance with a mean accuracy of 0.9827, mean sensitivity of 0.9684 and mean specificity of 0.9892 in combined diabetic foot detection and grading. Conclusion: To the best of our knowledge, this study sets a new state-of-the-art in plantar foot thermogram detection and grading, while being the first to implement a holistic multi-class classification and grading solution. Reliable automatic thermogram grading is a first step towards the development of smart health devices for DM patients.
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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.