ReviewBiosensors2022
Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review.
Review in Biosensors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.
What it found
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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
24 citing papers in PubMed, 4 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Artificial intelligence for detection, prediction, and severity classification of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Association of sarcopenia and muscle attenuation with clinical outcomes in patients with diabetic foot: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Diabetic foot prevention, assessment, and management using innovative smart wearable technology: a systematic review.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Pooled it
- Clinical Significance of Non-Invasive Skin Autofluorescence Measurement and AI Applications in Patients with Diabetic Foot Ulcers: A Scoping Review.Journal of personalized medicine · 2026Review
- AI-Powered Thermography for Diabetic Foot Risk Stratification: Multicenter Cross-Sectional Study.JMIR formative research · 2025Article
- Machine Learning-Driven E-Nose-Based Diabetes Detection: Sensor Selection and Feature Reduction Study.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence and Machine Learning in Diabetic Foot Ulcer Care: Advances in Diagnosis, Treatment, Prognosis, and Novel Therapeutic Strategies.Journal of diabetes science and technology · 2025Review
- Article
- Artificial Intelligence Algorithm to Screen for Diabetic Neuropathy: A Pilot Study.Biomedicines · 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
- Personalized and predictive strategies for diabetic foot ulcer prevention and therapeutic management: potential improvements through introducing Artificial Intelligence and wearable technology.Medicine and pharmacy reports · 2024Review
- GLI family zinc finger protein 2 promotes skin fibroblast proliferation and DNA damage repair by targeting the miR-200/ataxia telangiectasia mutated axis in diabetic wound healing.The Kaohsiung journal of medical sciences · 2024Article
- The role of machine learning in advancing diabetic foot: a review.Frontiers in endocrinology · 2024Review
- In-shoe plantar shear stress sensor design, calibration and evaluation for the diabetic foot.PloS one · 2024Article
- Integrating bioinformatics and multiple machine learning to identify mitophagy-related targets for the diagnosis and treatment of diabetic foot ulcers: evidence from transcriptome analysis and drug docking.Frontiers in molecular biosciences · 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
- Innovative Functional Biomaterials as Therapeutic Wound Dressings for Chronic Diabetic Foot Ulcers.International journal of molecular sciences · 2023Review
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.
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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.