ArticleSensors (Basel, Switzerland)2022
Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Frontiers in public health · 2025Pooled it
- AI-Enhanced System for Diabetic Foot Ulcer Localization and Classification Using Multi-Scale Neural CNN Model.Health science reports · 2026Article
- 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
- A novel thermal image based cold object detection and classification using machine learning algorithms.Scientific reports · 2026Article
- Improving Diabetic Foot Care With Infrared Thermography and Artificial Intelligence: A Review.Journal of diabetes science and technology · 2026Review
- 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
- Enhancing Early Detection of Diabetic Foot Ulcers Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2025Article
- Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.Biosensors · 2024Review
- Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification.Journal of diabetes and metabolic disorders · 2024Article
- Secure and efficient implementation of facial emotion detection for smart patient monitoring system.Quantitative biology (Beijing, China) · 2023Article
- Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning.Life (Basel, Switzerland) · 2023Article
- Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.Biosensors · 2022Review
- Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature.Sensors (Basel, Switzerland) · 2022Article
- Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data.Journal of personalized medicine · 2022Article
- Pedabarography May Play a Role in Foot Plantar Scanning in Acromegaly.International journal of clinical practice · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 6 institutions in 3 countries.
Funding
Abstract
Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.
Indexed as
Identifiers
What OpenQuestion holds
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.