The purpose of this study was to establish a deep learning model for automated sub-basal corneal nerve fiber (CNF) segmentation and evaluation with in vivo confocal microscopy (IVCM). Methods: A corneal nerve segmentation network (CNS-Net) was established with convolutional neural networks based on a deep learning algorithm for sub-basal corneal nerve segmentation and evaluation. CNS-Net was trained with 552 and tested on 139 labeled IVCM images as supervision information collected from July 2017 to December 2018 in Peking University Third Hospital. These images were labeled by three senior ophthalmologists with ImageJ software and then considered ground truth. The areas under the receiver operating characteristic curves (AUCs), mean average precision (mAP), sensitivity, and specificity were applied to evaluate the efficiency of corneal nerve segmentation. The relative deviation ratio (RDR) was leveraged to evaluate the accuracy of the corneal nerve fiber length (CNFL) evaluation task. Results: The model achieved an AUC of 0.96 (95% confidence interval [CI] = 0.935-0.983) and an mAP of 94% with minimum dice coefficient loss at 0.12. For our dataset, the sensitivity was 96% and specificity was 75% in the CNF segmentation task, and an RDR of 16% was reported in the CNFL evaluation task. Moreover, the model was able to segment and evaluate as many as 32 images per second, much faster than skilled ophthalmologists. Conclusions: We established a deep learning model, CNS-Net, which demonstrated a high accuracy and fast speed in sub-basal corneal nerve segmentation with IVCM. The results highlight the potential of the system in assisting clinical practice for corneal nerves segmentation and evaluation. Translational Relevance: The deep learning model for IVCM images may enable rapid segmentation and evaluation of the corneal nerve and may provide the basis for the diagnosis and treatment of ocular surface diseases associated with corneal nerves.

No abstract

Purpose: This study aimed to observe corneal subbasal nerves and Langerhans cells (LCs) using in vivo confocal microscopy (IVCM) in patients with dry eye, a tool for the evaluation of disease stage and severity and for treatment monitoring at the microstructural level. Methods: A total of 107 eyes from 62 patients were included. The Ocular Surface Disease Index (OSDI) questionnaire and other examinations were used to assess dry eye symptoms and signs. IVCM was performed to observe subbasal corneal nerves and LCs. Corneal nerves were graded using both objective and subjective methods. The correlations between dry eye symptoms and corneal nerve parameters, corneal nerve grading, and LC number were analyzed. Results: Corneal nerve length was negatively correlated with sensitivity to light [correlation coefficient (CC)= −0.24, P < 0.05]; nerve width was positively correlated with the OSDI score, painful eyes, and blurred vision (CC = 0.41, 0.23, and 0.46, respectively, all P < 0.05); and nerve tortuosity was positively correlated with sensitivity to light (CC = 0.23, P < 0.05). Moreover, both total objective and subjective grading scores were positively correlated with OSDI scores (CC = 0.48 and 0.27, respectively, both P < 0.05). LC number was found not to be significantly correlated with dry eye symptoms (P > 0.05). Conclusions: IVCM is a useful tool to evaluate corneal subbasal nerve changes in patients with dry eye. Detailed nerve grading could help to understand and evaluate the pathophysiologic conditions of the disease and could be used for further treatment follow-up in the future.

This study aimed to propose a comprehensive grading scale to evaluate different clinical manifestations in patients with varying severity of meibomian gland dysfunction (MGD) and analyze the correlations between the parameters of ocular surface impairment in MGD. A total of 63 patients with MGD were enrolled. Ten specific symptoms were evaluated each with a subjective score and total score was applied to grade the severity of MGD. Thirty-seven patients were diagnosed with mild, 19 with moderate, and 7 with severe MGD. Slit-lamp and keratography were used to assess the signs of ocular surface and meibomian gland (MG). In vivo confocal microscopy (IVCM) was performed to evaluate the corneal nerves and dendritic cells. The differences and correlations between symptoms, signs, and IVCM parameters were analyzed. Dryness, foreign body sensation, asthenopia, and photophobia were the most common and severe symptoms in our patients. The severe MGD group showed worse MG expressibility, Meibum score, Meiboscore, MG score, and higher nerve reflectivity ( P < .05). The mild MGD group showed higher nerve density ( P < .05). Total symptom score was negatively correlated with nerve density ( r = –0.374, P < .05), while positively correlated with nerve reflectivity and dendritic cell density ( r = 0.332 and 0.288, respectively, P < .05). MG score was correlated with nerve reflectivity ( r = 0.265, P < .05). The comprehensive grading scale was suitable for evaluating clinical manifestations in MGD of varying severity. The relationship between the specific symptoms, signs, and IVCM results concerning whole ocular surface impairment could help elucidate MGD pathophysiology and benefit evaluation or treatment in the future.