Dataset Information

Corneal confocal microscopy differentiates inflammatory from diabetic neuropathy.

ABSTRACT:

Background

Immune-mediated neuropathies, such as chronic inflammatory demyelinating polyneuropathy (CIDP) are treatable neuropathies. Among individuals with diabetic neuropathy, it remains a challenge to identify those individuals who develop CIDP. Corneal confocal microscopy (CCM) has been shown to detect corneal nerve fiber loss and cellular infiltrates in the sub-basal layer of the cornea. The objective of the study was to determine whether CCM can distinguish diabetic neuropathy from CIDP and whether CCM can detect CIDP in persons with coexisting diabetes.

Methods

In this multicenter, case-control study, participants with CIDP (n = 55) with (n = 10) and without (n = 45) diabetes; participants with diabetes (n = 58) with (n = 28) and without (n = 30) diabetic neuropathy, and healthy controls (n = 58) underwent CCM. Corneal nerve fiber density (CNFD), corneal nerve fiber length (CNFL), corneal nerve branch density (CNBD), and dendritic and non-dendritic cell density, with or without nerve fiber contact were quantified.

Results

Dendritic cell density in proximity to corneal nerve fibers was significantly higher in participants with CIDP with and without diabetes compared to participants with diabetic neuropathy and controls. CNFD, CNFL, and CNBD were equally reduced in participants with CIDP, diabetic neuropathy, and CIDP with diabetes.

Conclusions

An increase in dendritic cell density identifies persons with CIDP. CCM may, therefore, be useful to differentiate inflammatory from non-inflammatory diabetic neuropathy.

SUBMITTER: Fleischer M

PROVIDER: S-EPMC8033689 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionThe assessment of the corneal nerve fibre plexus with corneal confocal microscopy (CCM) is an upcoming but still experimental method in the diagnosis of early stage diabetic peripheral neuropathy (DPN). Using an innovative imaging technique-Heidelberg Retina Tomograph equipped with the Rostock Cornea Module (HRT-RCM) and EyeGuidance module (EG)-we were able to look at greater areas of subbasal nerve plexus (SNP) in order to increase the diagnostic accuracy. The aim of our study was to evaluate the usefulness of EG instead of single image analysis in diagnosis of early stage DPN.MethodsThis prospective study was performed on 60 patients with type 2 diabetes mellitus, classified equally into two subgroups based on neuropathy deficient score (NDS): patients without DPN (group 1) or with mild DPN (group 2). The following parameters were analysed in the two subgroups: corneal nerve fibre length (CNFL; mm/mm2), corneal nerve fibre density (CNFD; no./mm2), corneal nerve branch density (CNBD; no./mm2). Furthermore, we compared the data calculated with the novel mosaic, EG-based method with those received from single image analysis using different quantification tools.ResultsUsing EG we did not find a significant difference between group 1 and group 2: CNFL (16.81 ± 5.87 mm/mm2 vs. 17.19 ± 7.19 mm/mm2, p = 0.895), CNFD (254.05 ± 115.36 no./mm2 vs. 265.91 ± 161.63 no./mm2, p = 0.732) and CNBD (102.68 ± 62.28 no./mm2 vs. 115.38 ± 96.91 no./mm2, p = 0.541). No significant difference between the EG method of analysing the SNP and the single image analysis of 10 images per patient was detected.ConclusionOn the basis of our results it was not possible to differentiate between early stages of large nerve fibre DPN in patients with type 2 diabetes mellitus via SNP analysis. To improve sensitivity and specificity of this method newer technologies are under current evaluation.Trial registrationClinicalTrials.gov Identifier NCT05326958.

Project description:Aims/hypothesisCorneal confocal microscopy is a rapid non-invasive ophthalmic imaging technique that identifies peripheral and central neurodegenerative disease. Quantification of corneal sub-basal nerve plexus morphology, however, requires either time-consuming manual annotation or a less-sensitive automated image analysis approach. We aimed to develop and validate an artificial intelligence-based, deep learning algorithm for the quantification of nerve fibre properties relevant to the diagnosis of diabetic neuropathy and to compare it with a validated automated analysis program, ACCMetrics.MethodsOur deep learning algorithm, which employs a convolutional neural network with data augmentation, was developed for the automated quantification of the corneal sub-basal nerve plexus for the diagnosis of diabetic neuropathy. The algorithm was trained using a high-end graphics processor unit on 1698 corneal confocal microscopy images; for external validation, it was further tested on 2137 images. The algorithm was developed to identify total nerve fibre length, branch points, tail points, number and length of nerve segments, and fractal numbers. Sensitivity analyses were undertaken to determine the AUC for ACCMetrics and our algorithm for the diagnosis of diabetic neuropathy.ResultsThe intraclass correlation coefficients for our algorithm were superior to those for ACCMetrics for total corneal nerve fibre length (0.933 vs 0.825), mean length per segment (0.656 vs 0.325), number of branch points (0.891 vs 0.570), number of tail points (0.623 vs 0.257), number of nerve segments (0.878 vs 0.504) and fractals (0.927 vs 0.758). In addition, our proposed algorithm achieved an AUC of 0.83, specificity of 0.87 and sensitivity of 0.68 for the classification of participants without (n = 90) and with (n = 132) neuropathy (defined by the Toronto criteria).Conclusions/interpretationThese results demonstrated that our deep learning algorithm provides rapid and excellent localisation performance for the quantification of corneal nerve biomarkers. This model has potential for adoption into clinical screening programmes for diabetic neuropathy.Data availabilityThe publicly shared cornea nerve dataset (dataset 1) is available at http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Tortuosity%20Data%20Set.htm and http://bioimlab.dei.unipd.it/Corneal%20Nerve%20Data%20Set.htm.