Corneal nerve fractal dimension: a novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy

Objective: Corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, is a noninvasive and objective imaging biomarker for identifying small nerve fiber damage. We have evaluated the diagnostic performance of previously established CCM parameters to a novel automated measure of corne...

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Bibliographic Details
Main Authors: Chen, Xin, Graham, Jim, Petropoulos, Ioannis N., Ponirakis, Georgios, Asghar, Omar, Alam, Uazman, Marshall, Andrew, Ferdousi, Maryam, Azmi, Shazli, Efron, Nathan, Malik, Rayaz A.
Format: Article
Language:English
Published: Association for Research in Vision and Opthalmology 2018
Online Access:https://eprints.nottingham.ac.uk/50722/
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Summary:Objective: Corneal confocal microscopy (CCM), an in vivo ophthalmic imaging modality, is a noninvasive and objective imaging biomarker for identifying small nerve fiber damage. We have evaluated the diagnostic performance of previously established CCM parameters to a novel automated measure of corneal nerve complexity called the corneal nerve fiber fractal dimension (ACNFrD). Methods: A total of 176 subjects (84 controls and 92 patients with type 1 diabetes) with and without diabetic sensorimotor polyneuropathy (DSPN) underwent CCM. Fractal dimension analysis was performed on CCM images using purpose-built corneal nerve analysis software, and compared with previously established manual and automated corneal nerve fiber measurements. Results: Manual and automated subbasal corneal nerve fiber density (CNFD) (P < 0.0001), length (CNFL) (P < 0.0001), branch density (CNBD) (P < 0.05), and ACNFrD (P < 0.0001) were significantly reduced in patients with DSPN compared to patients without DSPN. The areas under the receiver operating characteristic curves for identifying DSPN were comparable: 0.77 for automated CNFD, 0.74 for automated CNFL, 0.69 for automated CNBD, and 0.74 for automated ACNFrD. Conclusions: ACNFrD shows comparable diagnostic efficiency to identify diabetic patients with and without DSPN.