Corneal Confocal Microscopy: A Biomarker for Diabetic Peripheral Neuropathy

Petropoulos, Ioannis N.; Ponirakis, Georgios; Ferdousi, Maryam; Azmi, Shazli; Kalteniece, Alise; Khan, Adnan; Gad, Hoda; Bashir, Bilal; Boulton, Andrew J.M.; Soran, Handrean; Malik, Rayaz A.

doi:10.1016/j.clinthera.2021.04.003

Cited by 35 publications

(37 citation statements)

References 120 publications

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“…CCM is a non-invasive ophthalmic imaging modality which may serve as a bona fide biomarker of diabetic neuropathy [ 36 ]. It has been posited as a game changer in the screening and diagnosis of diabetic and peripheral neuropathies [ 37 ]. Our study demonstrated two main findings: (1) the successful development of an AI-based algorithm without the need for nerve segmentation; and (2) it established accurate classification of individuals with and without peripheral neuropathy and healthy individuals.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

et al. 2021

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Aims/hypothesis We 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). Methods The 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. Results The 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), F1-score of 0.91 (95% CI 0.79, 1.0); PN−: recall of 0.85 (95% CI 0.62, 1.0), precision of 0.92 (95% CI 0.73, 1.0), F1-score of 0.88 (95% CI 0.71, 1.0); PN+: recall of 0.83 (95% CI 0.58, 1.0), precision of 1.0 (95% CI 1.0, 1.0), F1-score of 0.91 (95% CI 0.74, 1.0). The features displayed by the attribution methods demonstrated more corneal nerves in HV, a reduction in corneal nerves for PN− and an absence of corneal nerves for PN+ images. Conclusions/interpretation We 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. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

et al. 2021

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“…Furthermore, new evidence suggests these corneal nerve changes are strongly linked to neuropathic pain [ 126 ]. According to a recent review by Petropoulos et al [ 124 ], the last 20 years of research into corneal confocal microscopy has yielded sufficient evidence to classify corneal nerve loss as a biomarker for DN because it can predict incidence and progression, but this is yet to be recognized by regulators [ 124 ]. Corneal confocal microscopy may provide an easy and accurate test for detecting early DN, and further high-quality research should be undertaken to strengthen its evidence.…”

Section: Diagnostic Evaluationmentioning

confidence: 99%

“…Several novel diagnostic evaluation tools are under investigation. The most notable is corneal confocal microscopy, a noninvasive imaging technique that assesses small (C) fiber damage in the cornea, the body’s most densely innervated tissue [ 124 ]. A systematic review and meta-analysis of 13 studies (1680 participants) found that people with DN have significantly lower corneal nerve fiber density, length and branch density compared to healthy controls, implying that corneal confocal microscopy may be a useful tool in assessing early nerve damage [ 125 ].…”

Section: Diagnostic Evaluationmentioning

confidence: 99%

Prevention and Management Strategies for Diabetic Neuropathy

Smith

Normahani

Lane

et al. 2022

Life

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Diabetic neuropathy (DN) is a common complication of diabetes that is becoming an increasing concern as the prevalence of diabetes rapidly rises. There are several types of DN, but the most prevalent and studied type is distal symmetrical polyneuropathy, which is the focus of this review and is simply referred to as DN. It can lead to a wide range of sensorimotor and psychosocial symptoms and is a major risk factor for diabetic foot ulceration and Charcot neuropathic osteoarthropathy, which are associated with high rates of lower limb amputation and mortality. The prevention and management of DN are thus critical, and clinical guidelines recommend several strategies for these based on the best available evidence. This article aims to provide a narrative review of DN prevention and management strategies by discussing these guidelines and the evidence that supports them. First, the epidemiology and diverse clinical manifestations of DN are summarized. Then, prevention strategies such as glycemic control, lifestyle modifications and footcare are discussed, as well as the importance of early diagnosis. Finally, neuropathic pain management strategies and promising novel therapies under investigation such as neuromodulation devices and nutraceuticals are reviewed.

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“…Two recent trials with omega-3 fatty acid in patients with type 1 diabetes have demonstrated corneal nerve regeneration with no change in nerve conduction velocity, thermal thresholds, or autonomic nerve function [ 143 , 144 ]. Thus, CCM truly satisfies the FDA criteria for a biomarker and could play an important role as an endpoint in clinical trials of therapies for diabetic and other peripheral neuropathies, as well as central neurodegenerative diseases [ 145 ]. CCM has the potential to adopt the hub and spoke model currently employed the UK’s national diabetic retinopathy screening programme, whereby images are collected locally and interpreted at a central hub by AI.…”

Section: Future Clinical Applicationsmentioning

confidence: 99%

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship

Alam

Anson²,

Meng³

et al. 2022

JCM

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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.

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Corneal Confocal Microscopy: A Biomarker for Diabetic Peripheral Neuropathy

Cited by 35 publications

References 120 publications

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

Artificial intelligence utilising corneal confocal microscopy for the diagnosis of peripheral neuropathy in diabetes mellitus and prediabetes

Prevention and Management Strategies for Diabetic Neuropathy

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship

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