Eight months follow-up of corneal nerves and sensitivity after treatment with cenegermin for neurotrophic keratopathy

Backgroud: Cenegermin (Oxervate, Dompè Farmaceutici, Milan, IT), a recombinant human NGF, is a potentially healing new drug for neurotrophic keratopathy (NK), a rare but challenging disease affecting the cornea. To date, studies that evaluate its mid-term effect on corneal nerves and sensitivity are lacking. Objective: To evaluate the recovery and morphology of subbasal corneal nerves in patients treated with Cenegermin for NK and assess their correlation with corneal sensitivity. Methods: This prospective, observational case series study was carried out between May 2018 and August 2020 at the Ophthalmic Clinic of the University of Verona. Clinical evaluation, sensitivity, and in vivo confocal microscopy (IVCM) were performed in the central and all four corneal sectors at baseline, the end of therapy (8 weeks), and 2, 4, and 8 months after therapy. Consecutive patients with NK (stage 2-3), treated with Cenegermin (1 drop 6 times daily for 8 weeks), were enrolled. During each visit, Corneal nerve fiber length (CNFL), corneal nerve fiber total branch density (CTBD), corneal nerve fiber fractal dimension (CNFraD) and Cochet-Bonnet esthesiometry (CBE) were measured. Results: We enrolled 18 patients. Complete NK healing was noted in 14/18(78%) patients after 8 weeks of treatment; then in 14(78%), 15(83%), and 13(72%) patients at 2-, 4-, and 8-months, respectively. Starting at 8 weeks through 4-month follow-up there was progressive improvement in CBE in all corneal sectors (p ≤ 0.01), which continued thereafter. There was significant corneal nerve regrowth especially in the peripheral cornea: centripetal progression starting at 8 weeks (CNFL and CNFrad) and significant branching starting at 2 months (CTBD), which continued through to the end of follow up. Sector-coupled IVCM and CBE findings correlated at all evaluations (all r ≥ 0.62 starting at 2 months, with highest values in the peripheral sectors). Conclusions: After Cenegermin we observed a subbasal corneal nerve regeneration, a recovery of sensitivity and a lasting epithelial healing, suggesting that the effect of its action persists several months after discontinuation in patients with NK

Corneal dendritic cells and the subbasal nerve plexus following neurotoxic treatment with oxaliplatin or paclitaxel

Immune cell infiltration has been implicated in neurotoxic chemotherapy for cancer treatment. However, our understanding of immune processes is still incomplete and current methods of observing immune cells are time consuming or invasive. Corneal dendritic cells are potent antigen-presenting cells and can be imaged with in-vivo corneal confocal microscopy. Corneal dendritic cell densities and nerve parameters in patients treated with neurotoxic chemotherapy were investigated. Patients treated for cancer with oxaliplatin (n = 39) or paclitaxel (n = 48), 3 to 24 months prior to assessment were recruited along with 40 healthy controls. Immature (ImDC), mature (MDC) and total dendritic cell densities (TotalDC), and corneal nerve parameters were analyzed from in-vivo corneal confocal microscopy images. ImDC was increased in the oxaliplatin group (Median, Md = 22.7 cells/mm2) compared to healthy controls (Md = 10.1 cells/mm2, p = 0.001), but not in the paclitaxel group (Md = 10.6 cells/mm2). ImDC was also associated with higher oxaliplatin cumulative dose (r = 0.33, p = 0.04) and treatment cycles (r = 0.40, p = 0.01). There was no significant difference in MDC between the three groups (p > 0.05). Corneal nerve parameters were reduced in both oxaliplatin and paclitaxel groups compared to healthy controls (p < 0.05). There is evidence of elevation of corneal ImDC in oxaliplatin-treated patients. Further investigation is required to explore this potential link through longitudinal studies and animal or laboratory-based immunohistochemical research

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes

A cross-sectional study of ocular surface discomfort and corneal nerve dysfunction after paclitaxel treatment for cancer

Ocular surface dysfunction is common in patients receiving anti-cancer drug treatment. The effects of paclitaxel, a neurotoxic chemotherapeutic drug, on ocular surface discomfort associated with dry eye disease was investigated. Patients with cancer who had completed paclitaxel treatment between 3 and 24 months prior to assessment (n = 29) and age- and sex-matched healthy control subjects (n = 29) were recruited and assessed with the Ocular Surface Disease Index (OSDI) to measure ocular surface discomfort. In-vivo corneal confocal microscopy was used to evaluate corneal nerve parameters in the right eye. Peripheral neurotoxicity was assessed using patient-reported outcomes and clinical grading scales. The paclitaxel group had significantly worse OSDI total scores compared with controls (Median, Md = 19.3 and Md = 0, p = 0.007, respectively). Corneal nerve fiber and inferior whorl lengths were reduced in the paclitaxel group compared with controls (14.2 ± 4.0 and 14.4 ± 4.0 mm/mm2 vs. 16.4 ± 4.0 and 16.9 ± 4.9 mm/mm2, respectively, p = 0.04). When analyzed by presence of peripheral neuropathy, paclitaxel-treated patients with neuropathy showed worse OSDI total scores compared to those without peripheral neuropathy post-treatment (p = 0.001) and healthy controls (p < 0.001). More severe ocular discomfort and worse visual function was associated with greater peripheral neurotoxicity symptoms (r = 0.60, p = 0.001) and neuropathy severity (r = 0.49, p = 0.008), respectively. Patients who have been treated with paclitaxel have a higher risk of ocular surface discomfort associated with dry eye disease, particularly those with peripheral neuropathy. Future longitudinal studies should investigate the clinical impact of corneal nerve reduction in dry eye disease

Corneal dendritic cells and the subbasal nerve plexus following neurotoxic treatment with oxaliplatin or paclitaxel

Immune cell infiltration has been implicated in neurotoxic chemotherapy for cancer treatment. However, our understanding of immune processes is still incomplete and current methods of observing immune cells are time consuming or invasive. Corneal dendritic cells are potent antigen-presenting cells and can be imaged with in-vivo corneal confocal microscopy. Corneal dendritic cell densities and nerve parameters in patients treated with neurotoxic chemotherapy were investigated. Patients treated for cancer with oxaliplatin (n = 39) or paclitaxel (n = 48), 3 to 24 months prior to assessment were recruited along with 40 healthy controls. Immature (ImDC), mature (MDC) and total dendritic cell densities (TotalDC), and corneal nerve parameters were analyzed from in-vivo corneal confocal microscopy images. ImDC was increased in the oxaliplatin group (Median, Md = 22.7 cells/mm 2) compared to healthy controls (Md = 10.1 cells/mm 2, p = 0.001), but not in the paclitaxel group (Md = 10.6 cells/mm 2). ImDC was also associated with higher oxaliplatin cumulative dose (r = 0.33, p = 0.04) and treatment cycles (r = 0.40, p = 0.01). There was no significant difference in MDC between the three groups (p > 0.05). Corneal nerve parameters were reduced in both oxaliplatin and paclitaxel groups compared to healthy controls (p < 0.05). There is evidence of elevation of corneal ImDC in oxaliplatin-treated patients. Further investigation is required to explore this potential link through longitudinal studies and animal or laboratory-based immunohistochemical research

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 in dry eye disease

Dry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation

Development of Novel Diagnostic Tools for Dry Eye Disease using Infrared Meibography and In Vivo Confocal Microscopy

Dry eye disease (DED) is a multifactorial disease of the ocular surface where tear film instability, hyperosmolarity, neurosensory abnormalities, meibomian gland dysfunction, ocular surface inflammation and damage play a dedicated etiological role. Estimated 5 to 50% of the world population in different demographic locations, age and gender are currently affected by DED. The risk and occurrence of DED increases at a significant rate with age, which makes dry eye a major growing public health issue. DED not only impacts the patient’s quality of vision and life, but also creates a socio-economic burden of millions of euros per year. DED diagnosis and monitoring can be a challenging task in clinical practice due to the multifactorial nature and the poor correlation between signs and symptoms. Key clinical diagnostic tests and techniques for DED diagnosis include tearfilm break up time, tear secretion – Schirmer’s test, ocular surface staining, measurement of osmolarity, conjunctival impression cytology. However, these clinical diagnostic techniques are subjective, selective, require contact, and are unpleasant for the patient’s eye. Currently, new advances in different state-of-the-art imaging modalities provide non-invasive, non- or semi-contact, and objective parameters that enable objective evaluation of DED diagnosis. Among the different and constantly evolving imaging modalities, some techniques are developed to assess morphology and function of meibomian glands, and microanatomy and alteration of the different ocular surface tissues such as corneal nerves, immune cells, microneuromas, and conjunctival blood vessels. These clinical parameters cannot be measured by conventional clinical assessment alone. The combination of these imaging modalities with clinical feedback provides unparalleled quantification information of the dynamic properties and functional parameters of different ocular surface tissues. Moreover, image-based biomarkers provide objective, specific, and non / marginal contact diagnosis, which is faster and less unpleasant to the patient’s eye than the clinical assessment techniques. The aim of this PhD thesis was to introduced deep learning-based novel computational methods to segment and quantify meibomian glands (both upper and lower eyelids), corneal nerves, and dendritic cells. The developed methods used raw images, directly export from the clinical devices without any image pre-processing to generate segmentation masks. Afterward, it provides fully automatic morphometric quantification parameters for more reliable disease diagnosis. Noteworthily, the developed methods provide complete segmentation and quantification information for faster disease characterization. Thus, the developed methods are the first methods (especially for meibomian gland and dendritic cells) to provide complete morphometric analysis. Taken together, we have developed deep learning based automatic system to segment and quantify different ocular surface tissues related to DED namely, meibomian gland, corneal nerves, and dendritic cells to provide reliable and faster disease characterization. The developed system overcomes the current limitations of subjective image analysis and enables precise, accurate, reliable, and reproducible ocular surface tissue analysis. These systems have the potential to make an impact clinically and in the research environment by specifying faster disease diagnosis, facilitating new drug development, and standardizing clinical trials. Moreover, it will allow both researcher and clinicians to analyze meibomian glands, corneal nerves, and dendritic cells more reliably while reducing the time needed to analyze patient images significantly. Finally, the methods developed in this research significantly increase the efficiency of evaluating clinical images, thereby supporting and potentially improving diagnosis and treatment of ocular surface disease