Classification of Corneal Nerve Images Using Machine Learning Techniques

Recent research shows that small nerve fiber damage is an early detector of neuropathy. These small nerve fibers are present in the human cornea and can be visualized through the use of a corneal confocal microscope. A series of images can be acquired from the subbasal nerve plexus of the cornea. Before the images can be quantified for nerve loss, a human expert manually traces the nerves in the image and then classifies the image as having neuropathy or not. Some nerve tracing algorithms are available in the literature, but none of them are reported as being used in clinical practice. An alternate practice is to visually classify the image for neuropathy without quantification. In this paper, we evaluate the potential of various machine learning techniques for automating corneal nerve image classification. First, the images are down-sampled using discrete wavelet transform, filtering and a number of morphological operations. The resulting binary image is used for extracting characteristic features of the image. This is followed by training the classifier on the extracted features. The trained classifier is then used for predicting the state of the nerves in the images. Our experiments yield a classification accuracy of 0.91 reflecting the effectiveness of the proposed method

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

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease understanding and clinical decision-making. However, the grading of corneal nerve tortuosity remains a great challenge due to the lack of agreements on the definition and quantification of tortuosity. In this paper, we propose a fully automated deep learning method that performs image-level tortuosity grading of corneal nerves, which is based on CCM images and segmented corneal nerves to further improve the grading accuracy with interpretability principles. The proposed method consists of two stages: 1) A pre-trained feature extraction backbone over ImageNet is fine-tuned with a proposed novel bilinear attention (BA) module for the prediction of the regions of interest (ROIs) and coarse grading of the image. The BA module enhances the ability of the network to model long-range dependencies and global contexts of nerve fibers by capturing second-order statistics of high-level features. 2) An auxiliary tortuosity grading network (AuxNet) is proposed to obtain an auxiliary grading over the identified ROIs, enabling the coarse and additional gradings to be finally fused together for more accurate final results. The experimental results show that our method surpasses existing methods in tortuosity grading, and achieves an overall accuracy of 85.64% in four-level classification. We also validate it over a clinical dataset, and the statistical analysis demonstrates a significant difference of tortuosity levels between healthy control and diabetes group. We have released a dataset with 1500 CCM images and their manual annotations of four tortuosity levels for public access. The code is available at: https://github.com/iMED-Lab/TortuosityGrading

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

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

The impact of dry eye disease on corneal nerve parameters::A systematic review and meta‐analysis

Purpose: Dry eye disease (DED) is a growing global health problem with a significant impact on the quality of life of patients. While neurosensory abnormalities have been recognised as a contributor to DED pathophysiology, the potential role of in vivo corneal confocal microscopy in detecting nerve loss or damage remains unclear. This systematic review with meta‐analysis (PROSPERO registered CRD42022381861) investigated whether DED has an impact on sub‐basal corneal nerve parameters. Methods: PubMed, Embase and Web of Science Core Collection databases were searched from inception to 9 December 2022. Studies using laser scanning confocal microscopy to compare corneal nerve parameters of DED with healthy eyes were included. Study selection process and data extraction were performed by two independent members of the review team. Results: Twenty‐two studies with 916 participants with DED and 491 healthy controls were included, with 21 of these studies included in subsequent meta‐analyses. There was a decrease in total corneal nerve length (−3.85 mm/mm2; 95% CI −5.16, −2.55), corneal main nerve trunk density (−4.81 number/mm2; 95% CI −7.94, −1.68) and corneal nerve branch density (−15.52 number/mm2; 95% CI −27.20, −3.84) in DED eyes compared with healthy eyes, with subgroup analysis demonstrating that these differences were more evident in studies using NeuronJ software, a semi‐automated procedure. While this review found evidence of loss of corneal nerve parameters in eyes with DED compared with healthy controls, particularly with the use of a semi‐automated image analysis method, it is evident that there is substantial heterogeneity between studies in terms of corneal nerve imaging methodology. Conclusions: Standardisation is required in terms of terminology and analysis, with more research needed to potentially improve the clinical applicability and practicality of corneal nerve imaging. Further investigation is also required to confirm the diagnostic accuracy of this imaging modality and its potential for monitoring DED treatment efficacy