ReviewJournal of clinical medicine2022
Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed, 27 citations in OpenAlex.
- Risk Factors Associated with Corneal Nerve Fiber Length Reduction in Patients with Type 2 Diabetes.Journal of clinical medicine · 2025Article
- SuperCCM: An Open Source Python Toolkit for Automated Quantification of Corneal Nerve Fibers in Confocal Microscopy Images.Translational vision science & technology · 2025Article
- Review
- Characterising corneal changes in aniridia-related keratopathy using in vivo confocal microscopy and a self-supervised AI model.BMJ open ophthalmology · 2025Observational
- Segmentation and multiparametric evaluation of corneal whorl-like nerves for in vivo confocal microscopy images in dry eye disease.BMJ open ophthalmology · 2024Article
- Artificial-Intelligence-Enhanced Analysis of In Vivo Confocal Microscopy in Corneal Diseases: A Review.Diagnostics (Basel, Switzerland) · 2024Review
- Discontinuity third harmonic generation microscopy for label-free imaging and quantification of intraepidermal nerve fibers.Cell reports methods · 2024Article
- Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges.Neural regeneration research · 2024Article
- Knowledge, Awareness, and Attitude of Healthcare Stakeholders on Alzheimer's Disease and Dementia in Qatar.International journal of environmental research and public health · 2023Review
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Painful Diabetic Peripheral Neuropathy: Practical Guidance and Challenges for Clinical Management.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Review
- Article
- 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
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases.
Indexed as
Identifiers
What Socratic 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 Socratic graph.