Corneal confocal microscopy for the diagnosis of diabetic peripheral neuropathy: A systematic review and meta-analysis.
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ABSTRACT: Introduction
Corneal confocal microscopy (CCM) is a rapid non-invasive ophthalmic imaging technique that identifies corneal nerve fiber damage. Small studies suggest that CCM could be used to assess patients with diabetic peripheral neuropathy (DPN).Aim
To undertake a systematic review and meta-analysis assessing the diagnostic utility of CCM for sub-clinical DPN (DPN- ) and established DPN (DPN+ ).Data sources
Databases (PubMed, Embase, Central, ProQuest) were searched for studies using CCM in patients with diabetes up to April 2020.Study selection
Studies were included if they reported on at least one CCM parameter in patients with diabetes.Data extraction
Corneal nerve fiber density (CNFD), corneal nerve branch density (CNBD), corneal nerve fiber length (CNFL), and inferior whorl length (IWL) were compared between patients with diabetes with and without DPN and controls. Meta-analysis was undertaken using RevMan V.5.3.Data synthesis
Thirty-eight studies including ~4,000 participants were included in this meta-analysis. There were significant reductions in CNFD, CNBD, CNFL, and IWL in DPN- vs controls (P < 0.00001), DPN+ vs controls (P < 0.00001), and DPN+ vs DPN- (P < 0.00001).Conclusion
This systematic review and meta-analysis shows that CCM detects small nerve fiber loss in subclinical and clinical DPN and concludes that CCM has good diagnostic utility in DPN.
SUBMITTER: Gad H
PROVIDER: S-EPMC8756328 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundImmune-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.MethodsIn 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.ResultsDendritic 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.ConclusionsAn increase in dendritic cell density identifies persons with CIDP. CCM may, therefore, be useful to differentiate inflammatory from non-inflammatory diabetic neuropathy.
| S-EPMC8033689 | biostudies-literature
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.
| S-EPMC9587149 | biostudies-literature
Project description:Aims/hypothesisWe 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).MethodsThe 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.ResultsThe 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/interpretationWe 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.
| S-EPMC8803718 | biostudies-literature
Project description:Accurately quantifying the progression of diabetic peripheral neuropathy is key to identify individuals who will progress to foot ulceration and to power clinical intervention trials. We have undertaken detailed neuropathy phenotyping to assess the longitudinal utility of different measures of neuropathy in patients with diabetes. Nineteen patients with diabetes (age 52.5?±?14.7 years, duration of diabetes 26.0?±?13.8 years) and 19 healthy controls underwent assessment of symptoms and signs of neuropathy, quantitative sensory testing, autonomic nerve function, neurophysiology, intra-epidermal nerve fibre density (IENFD) and corneal confocal microscopy (CCM) to quantify corneal nerve fibre density (CNFD), branch density (CNBD) and fibre length (CNFL). Mean follow-up was 6.5 years. Glycated haemoglobin (p?=?0.04), low-density lipoprotein-cholesterol (LDL-C) (p?=?0.0009) and urinary albumin creatinine ratio (p?<?0.0001) improved. Neuropathy symptom profile (p?=?0.03), neuropathy disability score (p?=?0.04), vibration perception threshold (p?=?0.02), cold perception threshold (p?=?0.006), CNFD (p?=?0.03), CNBD (p?<?0.0001), CNFL (p?<?0.0001), IENFD (p?=?0.04), sural (p?=?0.02) and peroneal motor nerve conduction velocity (p?=?0.03) deteriorated significantly. Change (?) in CNFL correlated with ?CPT (p?=?0.006) and ?Expiration/Inspiration ratio (p?=?0.002) and ?IENFD correlated with ?CNFD (p?=?0.005), ?CNBD (p?=?0.02) and ?CNFL (p?=?0.01). This study shows worsening of diabetic neuropathy across a range of neuropathy measures, especially CCM, despite an improvement in HbA1c and LDL-C. It further supports the utility of CCM as a rapid, non-invasive surrogate measure of diabetic neuropathy.
| S-EPMC7820596 | biostudies-literature
Project description:This randomized controlled study used corneal confocal microscopy (CCM) to compare the efficacy of Mecobalamin intramuscular injections vs oral tablets in treating mild to moderate diabetic peripheral neuropathy (DPN) by detecting early nerve fiber repair. Enrolled patients were randomized approximately 1:1 to receive Mecobalamin intramuscular injections (0.5 mg/day, 3 times/week) or Mecobalamin oral tablets (1.5 mg/day) for 8 weeks. Primary outcome was change of inferior whorl length (IWL) from baseline. Secondary outcomes included changes of corneal nerve fibre length (CNFL), corneal nerve fibre density (CNFD), corneal nerve branch density (CNBD) and the Survey of Autonomic Symptoms (SAS). 15 (93.75%) patients in the injection group and 17 (89.47%) patients in the tablet group completed the study. The injection treatment significantly improved patients' IWL from baseline (21.64 ± 3.00 mm/mm2 vs 17.64 ± 4.83 mm/mm2, P < 0.01) while the tablet treatment didn't. Additionally, the injection treatment led to significantly improved CNFL, CNBD and SAS from baseline (all P < 0.05) while the tablet treatment did not. No patient experienced any adverse events. In conclusion, CCM is sensitive enough to detect the superior efficacy of 8-week Mecobalamin intramuscular injection treatment for DPN compared to the oral tablet treatment.ClinicalTrials.gov registration number: NCT04372316 (30/04/2020).
| S-EPMC8290034 | biostudies-literature
Project description:Neuropathic pain is believed to arise from damage to nociceptive C fibres in diabetic neuropathy (DN). We have utilised corneal confocal microscopy (CCM) to quantify the severity of small nerve fibre damage in relation to the severity of neuropathic pain and quality of life (QoL) in patients with and without painful DN. 30 controls and patients with painful (n = 78) and painless (n = 62) DN underwent assessment of large and small nerve fibre function, CCM, neuropathic symptoms (small fibre neuropathy symptom inventory questionnaire, neuropathic pain scale) and QoL (SF-36, pre-R-ODS and hospital anxiety and depression scale). Patients with painful compared to painless DN, had comparable neurophysiology and vibration perception, but lower corneal nerve fibre density (20.1 ± 0.87 vs. 24.13 ± 0.91, P = 0.005), branch density (44.4 ± 3.31 vs. 57.74 ± 3.98, P = 0.03), length (19.61 ± 0.81 vs. 22.77 ± 0.83, P = 0.01), inferior whorl length (18.03 ± 1.46 vs. 25.1 ± 1.95, P = 0.005) and cold sensation threshold (21.35 ± 0.99 vs. 26.08 ± 0.5, P < 0.0001) and higher warm sensation threshold (43.7 ± 0.49 vs. 41.37 ± 0.51, P = 0.004) indicative of small fibre damage. There was a significant association between all CCM parameters and the severity of painful neuropathic symptoms, depression score and QoL. CCM identifies small nerve fibre loss, which correlates with the severity of neuropathic symptoms and reduced QoL in patients with painful diabetic neuropathy.
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Project description:ObjectivesCorneal confocal microscopy (CCM) is a rapid, non-invasive, reproducible technique that quantifies small nerve fibres. We have compared the diagnostic capability of CCM against a range of established measures of nerve damage in patients with diabetic neuropathy.MethodsIn this cross sectional study, thirty subjects with Type 1 diabetes without neuropathy (T1DM), thirty one T1DM subjects with neuropathy (DSPN) and twenty seven non-diabetic healthy control subjects underwent detailed assessment of neuropathic symptoms and neurologic deficits, quantitative sensory testing (QST), electrophysiology, skin biopsy and corneal confocal microscopy (CCM).ResultsSubjects with DSPN were older (C vs T1DM vs DSPN: 41.0±14.9 vs 38.8±12.5 vs 53.3±11.9, P = 0.0002), had a longer duration of diabetes (P<0.0001), lower eGFR (P = 0.006) and higher albumin-creatinine ratio (P = 0.03) with no significant difference for HbA1c, BMI, lipids and blood pressure. Patients with DSPN were representative of subjects with diabetic neuropathy with clinical signs and symptoms of neuropathy and greater neuropathy deficits quantified by QST, electrophysiology, intra-epidermal nerve fibre density and CCM. Corneal nerve fibre density (CNFD) (Spearman's Rho = 0.60 P<0.0001) and IENFD (Spearman's Rho = 0.56 P<0.0001) were comparable when correlated with peroneal nerve conduction velocity. For the diagnosis of diabetic neuropathy the sensitivity for CNFD was 0.77 and specificity was 0.79 with an area under the ROC curve of 0.81. IENFD had a diagnostic sensitivity of 0.61, specificity of 0.80 and area under the ROC curve of 0.73.ConclusionsCCM is a valid accurate non-invasive method to identify small nerve fibre pathology and is able to diagnose DPN.
| S-EPMC5515394 | biostudies-literature
Project description:ObjectiveCorneal nerve fiber length (CNFL) has been shown in research studies to identify diabetic peripheral neuropathy (DPN). In this longitudinal diagnostic study, we assessed the ability of CNFL to predict the development of DPN.Research design and methodsFrom a multinational cohort of 998 participants with type 1 and type 2 diabetes, we studied the subset of 261 participants who were free of DPN at baseline and completed at least 4 years of follow-up for incident DPN. The predictive validity of CNFL for the development of DPN was determined using time-dependent receiver operating characteristic (ROC) curves.ResultsA total of 203 participants had type 1 and 58 had type 2 diabetes. Mean follow-up time was 5.8 years (interquartile range 4.2-7.0). New-onset DPN occurred in 60 participants (23%; 4.29 events per 100 person-years). Participants who developed DPN were older and had a higher prevalence of type 2 diabetes, higher BMI, and longer duration of diabetes. The baseline electrophysiology and corneal confocal microscopy parameters were in the normal range but were all significantly lower in participants who developed DPN. The time-dependent area under the ROC curve for CNFL ranged between 0.61 and 0.69 for years 1-5 and was 0.80 at year 6. The optimal diagnostic threshold for a baseline CNFL of 14.1 mm/mm2 was associated with 67% sensitivity, 71% specificity, and a hazard ratio of 2.95 (95% CI 1.70-5.11; P < 0.001) for new-onset DPN.ConclusionsCNFL showed good predictive validity for identifying patients at higher risk of developing DPN ∼6 years in the future.
| S-EPMC8740920 | biostudies-literature
Project description:BackgroundCurrently, diabetic peripheral neuropathy (DPN) is one of the most severe complications of diabetes mellitus (DM). Despite the seriousness of this problem, limited evidence is available on the prevalence of diabetic peripheral neuropathy among patients with diabetes mellitus in Ethiopia. In Ethiopia, there were no updated studies that estimate the national prevalence of DPN. Hence, this systematic review and meta-analysis provided a national prevalence of diabetic peripheral neuropathy among patients with diabetes mellitus in Ethiopia.MethodsThis study was submitted for registration with the International Prospective Register of Systematic Reviews (PROSPERO) in March 2020 and accepted with the registration number CRD42020173831. Different database searching engines were searched online to retrieve related articles, including PubMed, Scopus, Google Scholar, African Journals Online, World Health Organization (WHO) Afro Library, and Cochrane Review. The reviewers used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guideline in the reviewing process. In this systematic review and meta-analysis, all published and unpublished articles were analyzed. The reviewers used the random effects model to estimate the pooled prevalence of diabetic peripheral neuropathy among diabetes mellitus patients. The reviewers conducted the statistical analysis using the R version 3.5.3 and RStudio version 1.2.5033 software for Windows. The reviewers evaluated the heterogeneity across the included studies by the inconsistency index (I 2). The reviewers examined the publication bias by the funnel plot.ResultsThe search of the databases produced 245 papers. After checking the inclusion and exclusion criteria, 38 articles with 14029 total patients with diabetes mellitus were found suitable for the review. Except for three (retrospective cohort study), all studies were cross-sectional. The overall pooled prevalence of diabetic peripheral neuropathy was 22% (95% CI 18% to 26%). The subgroup analysis of diabetic peripheral neuropathy among patients with diabetes in the different regions was 23% (95% CI 17% to 29%) in Addis Ababa, 27% (95% CI 16% to 38%) in Oromia, 16% (95% CI 14% to 18%) in South nation and nationalities, and 15% (95% CI 6% to 24%) in Amhara.ConclusionsMore than one-fifth of patients with diabetes have diabetic peripheral neuropathy. According to this study, the prevalence of diabetic peripheral neuropathy in Ethiopia is considerably high. This evidence suggests that attention should be given to patients with diabetes in monitoring patients' blood glucose.
| S-EPMC7880716 | biostudies-literature
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.
| S-EPMC6946763 | biostudies-literature