Imaging of the human fundus in the clinical setting:past present and future

The human fundus is a complex structure that can be easily visualized and the world of ophthalmology is going through a golden era of new and exciting fundus imaging techniques; recent advances in technology have allowed a significant improvement in the imaging modalities clinicians have available to formulate a diagnostic and treatment plan for the patient, but there is constant on-going work to improve current technology and create new ideas in order to gather as much information as possible from the human fundus. In this article we shall summarize the imaging techniques available in the standard medical retina clinic (i.e. not limited to the research lab) and delineate the technologies that we believe will have a significant impact on the way clinicians will assess retinal and choroidal pathology in the not too distant future

Intellectual System Diagnostics Glaucoma

Glaucoma is a chronic eye disease that can lead to permanent vision loss. However, glaucoma is a difficult disease to diagnose because there is no pattern in the distribution of nerve fibers in the ocular fundus. Spectral analysis of the ocular fundus images was performed using the Eidos intelligent system. From the ACRIMA eye image database, 90.7% of healthy eye images were recognized with an average similarity score of 0.588 and 74.42% of glaucoma eye images with an average similarity score of 0.558. The reliability of eye image recognition can be achieved by increasing the number of digitized parameters of eye images obtained, for example, by optical coherence tomography. The research contribution is the digital processing of fundus graphic images by the intelligent system “Eidos”. The scientific contribution lies in the automation of the glaucoma diagnosis process using digitized data. The results of the study can be used at medical faculties of universities to carry out automated diagnostics of glaucoma

Retinal Imaging in Alzheimer’s Disease

Identifying biomarkers of Alzheimer's disease (AD) will accelerate the understanding of its pathophysiology, facilitate screening and risk stratification, and aid in developing new therapies. Developments in non-invasive retinal imaging technologies, including optical coherence tomography (OCT), OCT angiography and digital retinal photography, have provided a means to study neuronal and vascular structures in the retina in people with AD. Both qualitative and quantitative measurements from these retinal imaging technologies (eg, thinning of peripapillary retinal nerve fibre layer, inner retinal layer, and choroidal layer, reduced capillary density, abnormal vasodilatory response) have been shown to be associated with cognitive function impairment and risk of AD. The development of computer algorithms for respective retinal imaging methods has further enhanced the potential of retinal imaging as a viable tool for rapid, early detection and screening of AD. In this review, we present an update of current retinal imaging techniques and their potential applications in AD research. We also discuss the newer retinal imaging techniques and future directions in this expanding field

Machine Learning Approaches for Automated Glaucoma Detection using Clinical Data and Optical Coherence Tomography Images

Glaucoma is a multi-factorial, progressive blinding optic-neuropathy. A variety of factors, including genetics, vasculature, anatomy, and immune factors, are involved. Worldwide more than 80 million people are affected by glaucoma, and around 300,000 in Australia, where 50% remain undiagnosed. Untreated glaucoma can lead to blindness. Early detection by Artificial intelligence (AI) is crucial to accelerate the diagnosis process and can prevent further vision loss. Many proposed AI systems have shown promising performance for automated glaucoma detection using two-dimensional (2D) data. However, only a few studies had optimistic outcomes for glaucoma detection and staging. Moreover, the automated AI system still faces challenges in diagnosing at the clinicians’ level due to the lack of interpretability of the ML algorithms and integration of multiple clinical data. AI technology would be welcomed by doctors and patients if the "black box" notion is overcome by developing an explainable, transparent AI system with similar pathological markers used by clinicians as the sign of early detection and progression of glaucomatous damage. Therefore, the thesis aimed to develop a comprehensive AI model to detect and stage glaucoma by incorporating a variety of clinical data and utilising advanced data analysis and machine learning (ML) techniques. The research first focuses on optimising glaucoma diagnostic features by combining structural, functional, demographic, risk factor, and optical coherence tomography (OCT) features. The significant features were evaluated using statistical analysis and trained in ML algorithms to observe the detection performance. Three crucial structural ONH OCT features: cross-sectional 2D radial B-scan, 3D vascular angiography and temporal-superior-nasal-inferior-temporal (TSNIT) B-scan, were analysed and trained in explainable deep learning (DL) models for automated glaucoma prediction. The explanation behind the decision making of DL models were successfully demonstrated using the feature visualisation. The structural features or distinguished affected regions of TSNIT OCT scans were precisely localised for glaucoma patients. This is consistent with the concept of explainable DL, which refers to the idea of making the decision-making processes of DL models transparent and interpretable to humans. However, artifacts and speckle noise often result in misinterpretation of the TSNIT OCT scans. This research also developed an automated DL model to remove the artifacts and noise from the OCT scans, facilitating error-free retinal layers segmentation, accurate tissue thickness estimation and image interpretation. Moreover, to monitor and grade glaucoma severity, the visual field (VF) test is commonly followed by clinicians for treatment and management. Therefore, this research uses the functional features extracted from VF images to train ML algorithms for staging glaucoma from early to advanced/severe stages. Finally, the selected significant features were used to design and develop a comprehensive AI model to detect and grade glaucoma stages based on the data quantity and availability. In the first stage, a DL model was trained with TSNIT OCT scans, and its output was combined with significant structural and functional features and trained in ML models. The best-performed ML model achieved an area under the curve (AUC): 0.98, an accuracy of 97.2%, a sensitivity of 97.9%, and a specificity of 96.4% for detecting glaucoma. The model achieved an overall accuracy of 90.7% and an F1 score of 84.0% for classifying normal, early, moderate, and advanced-stage glaucoma. In conclusion, this thesis developed and proposed a comprehensive, evidence-based AI model that will solve the screening problem for large populations and relieve experts from manually analysing a slew of patient data and associated misinterpretation problems. Moreover, this thesis demonstrated three structural OCT features that could be added as excellent diagnostic markers for precise glaucoma diagnosis

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Exploring Deep Learning Techniques for Glaucoma Detection: A Comprehensive Review

Glaucoma is one of the primary causes of vision loss around the world, necessitating accurate and efficient detection methods. Traditional manual detection approaches have limitations in terms of cost, time, and subjectivity. Recent developments in deep learning approaches demonstrate potential in automating glaucoma detection by detecting relevant features from retinal fundus images. This article provides a comprehensive overview of cutting-edge deep learning methods used for the segmentation, classification, and detection of glaucoma. By analyzing recent studies, the effectiveness and limitations of these techniques are evaluated, key findings are highlighted, and potential areas for further research are identified. The use of deep learning algorithms may significantly improve the efficacy, usefulness, and accuracy of glaucoma detection. The findings from this research contribute to the ongoing advancements in automated glaucoma detection and have implications for improving patient outcomes and reducing the global burden of glaucoma

Computational models for stuctural analysis of retinal images

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe evaluation of retina structures has been of great interest because it could be used as a non-intrusive diagnosis in modern ophthalmology to detect many important eye diseases as well as cardiovascular disorders. A variety of retinal image analysis tools have been developed to assist ophthalmologists and eye diseases experts by reducing the time required in eye screening, optimising the costs as well as providing efficient disease treatment and management systems. A key component in these tools is the segmentation and quantification of retina structures. However, the imaging artefacts such as noise, intensity homogeneity and the overlapping tissue of retina structures can cause significant degradations to the performance of these automated image analysis tools. This thesis aims to provide robust and reliable automated retinal image analysis technique to allow for early detection of various retinal and other diseases. In particular, four innovative segmentation methods have been proposed, including two for retinal vessel network segmentation, two for optic disc segmentation and one for retina nerve fibre layers detection. First, three pre-processing operations are combined in the segmentation method to remove noise and enhance the appearance of the blood vessel in the image, and a Mixture of Gaussians is used to extract the blood vessel tree. Second, a graph cut segmentation approach is introduced, which incorporates the mechanism of vectors flux into the graph formulation to allow for the segmentation of very narrow blood vessels. Third, the optic disc segmentation is performed using two alternative methods: the Markov random field image reconstruction approach detects the optic disc by removing the blood vessels from the optic disc area, and the graph cut with compensation factor method achieves that using prior information of the blood vessels. Fourth, the boundaries of the retinal nerve fibre layer (RNFL) are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. The strong experimental results of our retinal blood vessel segmentation methods including Mixture of Gaussian, Graph Cut achieved an average accuracy of 94:33%, 94:27% respectively. Our optic disc segmentation methods including Markov Random Field and Compensation Factor also achieved an average sensitivity of 92:85% and 85:70% respectively. These results obtained on several public datasets and compared with existing methods have shown that our proposed methods are robust and efficient in the segmenting retinal structures such the blood vessels and the optic disc.Brunel University Londonhttp://bura.brunel.ac.uk/bitstream/2438/10387/1/FulltextThesis.pd

Deep learning in ophthalmology: The technical and clinical considerations

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally