ReviewFrontiers in physiology2022
A review of non-invasive sensors and artificial intelligence models for diabetic foot monitoring.
Review in Frontiers in physiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- AI-Enhanced System for Diabetic Foot Ulcer Localization and Classification Using Multi-Scale Neural CNN Model.Health science reports · 2026Article
- Wearable Technologies for Diabetic Foot Ulcer Monitoring and Risk Prediction: Systematic Review.JMIR diabetes · 2026Review
- DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer classification.Frontiers in digital health · 2026Article
- AI-Powered Thermography for Diabetic Foot Risk Stratification: Multicenter Cross-Sectional Study.JMIR formative research · 2025Article
- Biosensors integrated within wearable devices for monitoring chronic wound status.APL bioengineering · 2025Article
- Unravelling the cardio-renal-metabolic-foot connection in people with diabetes-related foot ulceration: a narrative review.Cardiovascular diabetology · 2024Review
- Artificial intelligence's suggestions for level of amputation in diabetic foot ulcers are highly correlated with those of clinicians, only with exception of hindfoot amputations.International wound journal · 2024Article
- XAI-FusionNet: Diabetic foot ulcer detection based on multi-scale feature fusion with explainable artificial intelligence.Heliyon · 2024Article
- Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning.Biomedical engineering online · 2024Article
- Emerging technologies for the management of diabetic foot ulceration: a review.Frontiers in clinical diabetes and healthcare · 2024Review
- Leveraging smart image processing techniques for early detection of foot ulcers using a deep learning network.Polish journal of radiology · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic foot complications have multiple adverse effects in a person's quality of life. Yet, efficient monitoring schemes can mitigate or postpone any disorders, mainly by early detecting regions of interest. Nowadays, optical sensors and artificial intelligence (AI) tools can contribute efficiently to such monitoring processes. In this work, we provide information on the adopted imaging schemes and related optical sensors on this topic. The analysis considers both the physiology of the patients and the characteristics of the sensors. Currently, there are multiple approaches considering both visible and infrared bands (multiple ranges), most of them coupled with various AI tools. The source of the data (sensor type) can support different monitoring strategies and imposes restrictions on the AI tools that should be used with. This review provides a comprehensive literature review of AI-assisted DFU monitoring methods. The paper presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and the challenges for transferring these methods into a practical and trustworthy framework for sufficient remote management of the patients.
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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 Socratic graph.