ArticleDiabetologia2022
Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes.
Article in Diabetologia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed, 66 citations in OpenAlex.
- Shared mechanisms of musculoskeletal dysfunction in type 2 diabetes mellitus: Insights into future therapeutic directions (Review).International journal of molecular medicine · 2026Review
- Corneal nerve imaging in systemic neuropathy: A minireview of experimental and precision medicine applications.World journal of experimental medicine · 2026Review
- A Review of the Use of Artificial Intelligence in Ophthalmology Imaging: Approximation to Ocular Histopathology.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Review
- Innovative strategies for diabetic peripheral neuropathy: From clinical management to emerging bioengineering solutions.Bioactive materials · 2026Review
- Corneal Innervation Research at a Crossroads: A Tool-Driven Roadmap for the Future.Investigative ophthalmology & visual science · 2026Review
- Algor-Ethics in Diabetes Care: Mapping the Route.Diabetes/metabolism research and reviews · 2026Review
- AI-Assisted detection of corneal nerve structural abnormalities in early diabetic keratopathy: development and validation of a deep learning framework.Scientific reports · 2026Article
- Diabetes and its complications: molecular mechanisms, prevention and treatment.Signal transduction and targeted therapy · 2026Review
- SuperCCM: An Open Source Python Toolkit for Automated Quantification of Corneal Nerve Fibers in Confocal Microscopy Images.Translational vision science & technology · 2025Article
- Review
- Article
- Clinical Applications of Artificial Intelligence in Corneal Diseases.Vision (Basel, Switzerland) · 2025Review
- Characterising corneal changes in aniridia-related keratopathy using in vivo confocal microscopy and a self-supervised AI model.BMJ open ophthalmology · 2025Observational
- Artificial Intelligence Algorithm to Screen for Diabetic Neuropathy: A Pilot Study.Biomedicines · 2025Article
- Modern Technologies inSovremennye tekhnologii v meditsine · 2025Review
- Artificial Intelligence to Diagnose Complications of Diabetes.Journal of diabetes science and technology · 2025Review
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
- Polyneuropathy in Cerebrotendinous Xanthomatosis: Diagnostic Challenges and Potential for Therapeutic Intervention.Brain sciences · 2024Review
- Applications of Artificial Intelligence in Ophthalmology: Glaucoma, Cornea, and Oculoplastics.Cureus · 2024Review
- Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.Cardiovascular diabetology · 2024Observational
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
aims/hypothesisWe aimed to develop an artificial intelligence (AI)-based deep learning algorithm (DLA) applying attribution methods without image segmentation to corneal confocal microscopy images and to accurately classify peripheral neuropathy (or lack of).
methodsThe AI-based DLA utilised convolutional neural networks with data augmentation to increase the algorithm's generalisability. The algorithm was trained using a high-end graphics processor for 300 epochs on 329 corneal nerve images and tested on 40 images (1 image/participant). Participants consisted of healthy volunteer (HV) participants (n = 90) and participants with type 1 diabetes (n = 88), type 2 diabetes (n = 141) and prediabetes (n = 50) (defined as impaired fasting glucose, impaired glucose tolerance or a combination of both), and were classified into HV, those without neuropathy (PN-) (n = 149) and those with neuropathy (PN+) (n = 130). For the AI-based DLA, a modified residual neural network called ResNet-50 was developed and used to extract features from images and perform classification. The algorithm was tested on 40 participants (15 HV, 13 PN-, 12 PN+). Attribution methods gradient-weighted class activation mapping (Grad-CAM), Guided Grad-CAM and occlusion sensitivity displayed the areas within the image that had the greatest impact on the decision of the algorithm.
resultsThe results were as follows: HV: recall of 1.0 (95% CI 1.0, 1.0), precision of 0.83 (95% CI 0.65, 1.0), F CONCLUSIONS/
interpretationWe demonstrate promising results in the rapid classification of peripheral neuropathy using a single corneal image. A large-scale multicentre validation study is required to assess the utility of AI-based DLA in screening and diagnostic programmes for diabetic neuropathy.
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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.