ArticleSensors (Basel, Switzerland)2020
Deep Learning Classification for Diabetic Foot Thermograms.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Frontiers in public health · 2025Pooled it
- Skin Temperature of Acupoints in Health and Disease: A Systematic Review.Journal of integrative and complementary medicine · 2022Pooled 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
- An integrated design for segmentation and classification of diabetic foot ulcers using thermography images.Journal of diabetes and metabolic disorders · 2026Article
- NURSE-AI: A Nurse-by-Design Framework for Multi-Sensor, AI-Enabled Chronic Wound Assessment in Community Healthcare.Sensors (Basel, Switzerland) · 2026Article
- 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 Image-Based Diagnosis of Diabetic Foot Ulcers Using Deep Learning and Machine Learning: A Systematic Review.Biomedicines · 2025Review
- AI-Powered Thermography for Diabetic Foot Risk Stratification: Multicenter Cross-Sectional Study.JMIR formative research · 2025Article
- Enhancing Early Detection of Diabetic Foot Ulcers Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
- A feature explainability-based deep learning technique for diabetic foot ulcer identification.Scientific reports · 2025Article
- Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.Biosensors · 2024Review
- A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning.Scientific reports · 2024Article
- Differential Diagnosis of Diabetic Foot Osteomyelitis and Charcot Neuropathic Osteoarthropathy with Deep Learning Methods.Journal of imaging informatics in medicine · 2024Article
- Classification of Diabetic Foot Ulcers from Images Using Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2024Article
- Classification and Determination of Severity of Corneal Ulcer with Vision Transformer Based on the Analysis of Public Image Dataset of Fluorescein-Stained Corneas.Diagnostics (Basel, Switzerland) · 2024Article
- Diabetes management in the era of artificial intelligence.Archives of medical sciences. Atherosclerotic diseases · 2024Article
- State-of-the-Art Features for Early-Stage Detection of Diabetic Foot Ulcers Based on Thermograms.Biomedicines · 2023Article
- Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to the World Health Organization (WHO), Diabetes Mellitus (DM) is one of the most prevalent diseases in the world. It is also associated with a high mortality index. Diabetic foot is one of its main complications, and it comprises the development of plantar ulcers that could result in an amputation. Several works report that thermography is useful to detect changes in the plantar temperature, which could give rise to a higher risk of ulceration. However, the plantar temperature distribution does not follow a particular pattern in diabetic patients, thereby making it difficult to measure the changes. Thus, there is an interest in improving the success of the analysis and classification methods that help to detect abnormal changes in the plantar temperature. All this leads to the use of computer-aided systems, such as those involved in artificial intelligence (AI), which operate with highly complex data structures. This paper compares machine learning-based techniques with Deep Learning (DL) structures. We tested common structures in the mode of transfer learning, including AlexNet and GoogleNet. Moreover, we designed a new DL-structure, which is trained from scratch and is able to reach higher values in terms of accuracy and other quality measures. The main goal of this work is to analyze the use of AI and DL for the classification of diabetic foot thermograms, highlighting their advantages and limitations. To the best of our knowledge, this is the first proposal of DL networks applied to the classification of diabetic foot thermograms. The experiments are conducted over thermograms of DM and control groups. After that, a multi-level classification is performed based on a previously reported thermal change index. The high accuracy obtained shows the usefulness of AI and DL as auxiliary tools to aid during the medical diagnosis.
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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.