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

Xin Chen, Jim Graham, Ioannis N. Petropoulos, Georgios Ponirakis, Omar Asghar, Uazman Alam, Andrew Marshall, Maryam Ferdousi, Shazli Azmi, Nathan Efron, Rayaz Malik

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1113-1118
Number of pages6
JournalInvestigative Ophthalmology and Visual Science
Volume59
Issue number2
DOIs
Publication statusPublished - 1 Feb 2018

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Fractals
Diabetic Neuropathies
Nerve Fibers
Confocal Microscopy
Type 1 Diabetes Mellitus
ROC Curve
Software
Biomarkers

Keywords

  • Corneal confocal microscopy
  • Diabetic neuropathy
  • Image segmentation
  • Nerve fiber quantification

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

Cite this

Corneal nerve fractal dimension : A novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy. / Chen, Xin; Graham, Jim; Petropoulos, Ioannis N.; Ponirakis, Georgios; Asghar, Omar; Alam, Uazman; Marshall, Andrew; Ferdousi, Maryam; Azmi, Shazli; Efron, Nathan; Malik, Rayaz.

In: Investigative Ophthalmology and Visual Science, Vol. 59, No. 2, 01.02.2018, p. 1113-1118.

Research output: Contribution to journalArticle

Chen, Xin ; Graham, Jim ; Petropoulos, Ioannis N. ; Ponirakis, Georgios ; Asghar, Omar ; Alam, Uazman ; Marshall, Andrew ; Ferdousi, Maryam ; Azmi, Shazli ; Efron, Nathan ; Malik, Rayaz. / Corneal nerve fractal dimension : A novel corneal nerve metric for the diagnosis of diabetic sensorimotor polyneuropathy. In: Investigative Ophthalmology and Visual Science. 2018 ; Vol. 59, No. 2. pp. 1113-1118.
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AB - 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.

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