Neuropathy Classification of Corneal Nerve Images Using Artificial Intelligence

Nerve variations in the human cornea have been associated with alterations in the neuropathy state of a patient suffering from chronic diseases. For some diseases, such as diabetes, detection of neuropathy prior to visible symptoms is important, whereas for others, such as multiple sclerosis, early prediction of disease worsening is crucial. As current methods fail to provide early diagnosis of neuropathy, in vivo corneal confocal microscopy enables very early insight into the nerve damage by illuminating and magnifying the human cornea. This non-invasive method captures a sequence of images from the corneal sub-basal nerve plexus. Current practices of manual nerve tracing and classification impede the advancement of medical research in this domain. Since corneal nerve analysis for neuropathy is in its initial stages, there is a dire need for process automation. To address this limitation, we seek to automate the two stages of this process: nerve segmentation and neuropathy classification of images. For nerve segmentation, we compare the performance of two existing solutions on multiple datasets to select the appropriate method and proceed to the classification stage. Consequently, we approach neuropathy classification of the images through artificial intelligence using Adaptive Neuro-Fuzzy Inference System, Support Vector Machines, Naïve Bayes and k-nearest neighbors. We further compare the performance of machine learning classifiers with deep learning. We ascertained that nerve segmentation using convolutional neural networks provided a significant improvement in sensitivity and false negative rate by at least 5% over the state-of-the-art software. For classification, ANFIS yielded the best classification accuracy of 93.7% compared to other classifiers. Furthermore, for this problem, machine learning approaches performed better in terms of classification accuracy than deep learning

Artificial Intelligence and Corneal Confocal Microscopy: The Start of a Beautiful Relationship.

Corneal confocal microscopy (CCM) is a rapid non-invasive in vivo ophthalmic imaging technique that images the cornea. Historically, it was utilised in the diagnosis and clinical management of corneal epithelial and stromal disorders. However, over the past 20 years, CCM has been increasingly used to image sub-basal small nerve fibres in a variety of peripheral neuropathies and central neurodegenerative diseases. CCM has been used to identify subclinical nerve damage and to predict the development of diabetic peripheral neuropathy (DPN). The complex structure of the corneal sub-basal nerve plexus can be readily analysed through nerve segmentation with manual or automated quantification of parameters such as corneal nerve fibre length (CNFL), nerve fibre density (CNFD), and nerve branch density (CNBD). Large quantities of 2D corneal nerve images lend themselves to the application of artificial intelligence (AI)-based deep learning algorithms (DLA). Indeed, DLA have demonstrated performance comparable to manual but superior to automated quantification of corneal nerve morphology. Recently, our end-to-end classification with a 3 class AI model demonstrated high sensitivity and specificity in differentiating healthy volunteers from people with and without peripheral neuropathy. We believe there is significant scope and need to apply AI to help differentiate between peripheral neuropathies and also central neurodegenerative disorders. AI has significant potential to enhance the diagnostic and prognostic utility of CCM in the management of both peripheral and central neurodegenerative diseases

Peripheral neuropathy and altered cobalamin metabolism in Parkinson's disease and other movement disorders

Peripheral neuropathy is a disorder of the peripheral nervous system (PNS) that may carry different underlying aetiologies. Studies have suggested PNS involvement may be prevalent in neurological diseases that traditionally have been regarded primarily as disorders of the central nervous system (CNS), including the neurodegenerative disorder Parkinson’s disease (PD). In PD, an increased prevalence of large and/or small fiber neuropathy has been reported. Underlying associations with biochemical signs of disturbed vitamin B12 (cobalamin) metabolism have been suggested, and proposed to be driven by chronic exposure to treatment with levodopa. However, peripheral neuropathy as an intrinsic disease feature has also been suggested, possibly driven by peripheral neurodegeneration associated with PD. We investigated the prevalence of clinical signs of peripheral neuropathy in a levodopa treated population followed at Karolinska University Hospital. Assessments included biochemical, genetic, and clinical evaluations. We observed a significantly higher prevalence of clinical signs indicative of a peripheral neuropathy, as assessed by the Utah Early Neuropathy Scale (UENS), in PD patients relative to controls. Furthermore, an association between UENS scores and plasma levels of homocysteine, an amino acid involved in the cobalamin dependent methionine cycle, was demonstrated.(Study I) In Study II we explored if Restless legs syndrome (RLS) may constitute a clinical expression of peripheral neuropathy in patients with PD and co-existent RLS. PNS assessments included both functional and structural evaluations of large and small fibers. More specifically, we employed the UENS, nerve conduction studies, quantitative sensory testing, and a novel method visualising the small fibers of the corneal subbasal nerve plexus, in vivo corneal confocal microscopy. An association between PNS assessments and the clinical expression of RLS, in the setting of PD, could not be demonstrated. This finding argues against a peripheral degenerative aetiology of RLS in this context. However, associations between PNS assessments and direct or indirect measures of disease burden in PD were demonstrated. This may possibly support the notion that PNS pathology, to some extent, could reflect ongoing CNS pathology in PD. Previous studies have highlighted a possible role of altered cobalamin metabolism also in the setting of CNS manifestations in PD. In Study IV we characterized an adult patient with PD demonstrating a rare biochemical alteration, related to the cobalamin dependent metabolic pathway that takes place in the mitochondrial compartment. We present the underlying genetic variants, including a novel variant, presumed to drive this alteration, and discuss possible clinical implications. Gaucher disease (GD) is a hereditary lysosomal storage disorder that, to some extent, shares genetic background with PD. An increased prevalence of both small and large fiber neuropathy has been associated with GD. We examined patients with GD type 1 followed at Karolinska University Hospital and patients with the Norrbottnian GD type 3 followed at Sunderby Region Hospital. We assessed symptoms and clinical signs compatible with a peripheral neuropathy, followed by electrodiagnostic testing in selected cases. Acknowledging small sample size, we believe our study may support the notion that small fiber neuropathy could represent an inherent disease feature in GD type 1, but argues against this being a prevalent finding in Norrbottnian GD type 3. We suggest that the recognition of an ongoing small fiber neuropathy in this disease may harbour treatment implications with regard to pain management.(Study III) Hereditary spastic paraparesis (HSP) constitutes a group of genetic movement disorders with vast phenotypic and genotypic hetereogeneity. We characterized the clinical phenotype, PNS involvement, and cerebrospinal fluid findings in two families with HSP-KIF5A. We confirm previous reports of inter- and intrafamilial variability of the clinical phenotype. Furthermore, we argue that elevated cerebrospinal fluid neurofilament light chain is not a mandatory finding in this upper motor neuron disease.(Study V) In conclusion, I suggest monitoring and treatment of biochemical signs of altered cobalamin metabolism in PD may serve a protective role with regard to the PNS. I speculate that PNS pathology in PD may reflect both levodopa mediated effects and manifestations inherent to the disease itself, possibly with a predilection for large and small fibers respectively. Thus, I believe future studies addressing the potential biomarker role of PNS assessments, as a surrogate marker of general disease progression in PD, are warranted. Such studies must account for the possibility of clouding effects related to altered cobalamin metabolism mediated by levodopa