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. 2014 Apr 3;55(4):2071-8.
doi: 10.1167/iovs.13-13787.

Rapid automated diagnosis of diabetic peripheral neuropathy with in vivo corneal confocal microscopy

Ioannis N Petropoulos  1 , Uazman Alam, Hassan Fadavi, Andrew Marshall, Omar Asghar, Mohammad A Dabbah, Xin Chen, James Graham, Georgios Ponirakis, Andrew J M Boulton, Mitra Tavakoli, Rayaz A Malik
Affiliations

Rapid automated diagnosis of diabetic peripheral neuropathy with in vivo corneal confocal microscopy

Ioannis N Petropoulos et al. Invest Ophthalmol Vis Sci. .

Abstract

Purpose: To assess the diagnostic validity of a fully automated image analysis algorithm of in vivo confocal microscopy images in quantifying corneal subbasal nerves to diagnose diabetic neuropathy.

Methods: One hundred eighty-six patients with type 1 and type 2 diabetes mellitus (T1/T2DM) and 55 age-matched controls underwent assessment of neuropathy and bilateral in vivo corneal confocal microscopy (IVCCM). Corneal nerve fiber density (CNFD), branch density (CNBD), and length (CNFL) were quantified with expert, manual, and fully-automated analysis. The areas under the curve (AUC), odds ratios (OR), and optimal thresholds to rule out neuropathy were estimated for both analysis methods.

Results: Neuropathy was detected in 53% of patients with diabetes. A significant reduction in manual and automated CNBD (P < 0.001) and CNFD (P < 0.0001), and CNFL (P < 0.0001) occurred with increasing neuropathic severity. Manual and automated analysis methods were highly correlated for CNFD (r = 0.9, P < 0.0001), CNFL (r = 0.89, P < 0.0001), and CNBD (r = 0.75, P < 0.0001). Manual CNFD and automated CNFL were associated with the highest AUC, sensitivity/specificity and OR to rule out neuropathy.

Conclusions: Diabetic peripheral neuropathy is associated with significant corneal nerve loss detected with IVCCM. Fully automated corneal nerve quantification provides an objective and reproducible means to detect human diabetic neuropathy.

Keywords: corneal confocal microscopy; diabetes; diabetic neuropathy.

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Figures

Figure 1
Figure 1
An IVCCM image of a control subject analyzed using (A) manual expert and (B) fully-automated image analysis to quantify corneal subbasal nerve morphology in DSPN. Use of either quantification method results in the detection of comparable structures in the image.
Figure 2
Figure 2
Receiver operating characteristic curves for manual (solid black) and automated (red) CNFD (A), CNBD (B), and CNFL (C). Corneal nerve fiber density and CNFL showed the highest validity to diagnose DSPN with comparable AUCs (no significant difference). Manual CNFD and automated CNFL were associated with the highest OR.
See this image and copyright information in PMC

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