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

Detection and Classification of Diabetic Retinopathy Pathologies in Fundus Images

Diabetic Retinopathy (DR) is a disease that affects up to 80% of diabetics around the world. It is the second greatest cause of blindness in the Western world, and one of the leading causes of blindness in the U.S. Many studies have demonstrated that early treatment can reduce the number of sight-threatening DR cases, mitigating the medical and economic impact of the disease. Accurate, early detection of eye disease is important because of its potential to reduce rates of blindness worldwide. Retinal photography for DR has been promoted for decades for its utility in both disease screening and clinical research studies. In recent years, several research centers have presented systems to detect pathology in retinal images. However, these approaches apply specialized algorithms to detect specific types of lesion in the retina. In order to detect multiple lesions, these systems generally implement multiple algorithms. Furthermore, some of these studies evaluate their algorithms on a single dataset, thus avoiding potential problems associated with the differences in fundus imaging devices, such as camera resolution. These methodologies primarily employ bottom-up approaches, in which the accurate segmentation of all the lesions in the retina is the basis for correct determination. A disadvantage of bottom-up approaches is that they rely on the accurate segmentation of all lesions in order to measure performance. On the other hand, top-down approaches do not depend on the segmentation of specific lesions. Thus, top-down methods can potentially detect abnormalities not explicitly used in their training phase. A disadvantage of these methods is that they cannot identify specific pathologies and require large datasets to build their training models. In this dissertation, I merged the advantages of the top-down and bottom-up approaches to detect DR with high accuracy. First, I developed an algorithm based on a top-down approach to detect abnormalities in the retina due to DR. By doing so, I was able to evaluate DR pathologies other than microaneurysms and exudates, which are the main focus of most current approaches. In addition, I demonstrated good generalization capacity of this algorithm by applying it to other eye diseases, such as age-related macular degeneration. Due to the fact that high accuracy is required for sight-threatening conditions, I developed two bottom-up approaches, since it has been proven that bottom-up approaches produce more accurate results than top-down approaches for particular structures. Consequently, I developed an algorithm to detect exudates in the macula. The presence of this pathology is considered to be a surrogate for clinical significant macular edema (CSME), a sight-threatening condition of DR. The analysis of the optic disc is usually not taken into account in DR screening systems. However, there is a pathology called neovascularization that is present in advanced stages of DR, making its detection of crucial clinical importance. In order to address this problem, I developed an algorithm to detect neovascularization in the optic disc. These algorithms are based on amplitude-modulation and frequency-modulation (AM-FM) representations, morphological image processing methods, and classification algorithms. The methods were tested on a diverse set of large databases and are considered to be the state-of the art in this field

Automated retinal analysis

Diabetes is a chronic disease affecting over 2% of the population in the UK [1]. Long-term complications of diabetes can affect many different systems of the body including the retina of the eye. In the retina, diabetes can lead to a disease called diabetic retinopathy, one of the leading causes of blindness in the working population of industrialised countries. The risk of visual loss from diabetic retinopathy can be reduced if treatment is given at the onset of sight-threatening retinopathy. To detect early indicators of the disease, the UK National Screening Committee have recommended that diabetic patients should receive annual screening by digital colour fundal photography [2]. Manually grading retinal images is a subjective and costly process requiring highly skilled staff. This thesis describes an automated diagnostic system based oil image processing and neural network techniques, which analyses digital fundus images so that early signs of sight threatening retinopathy can be identified. Within retinal analysis this research has concentrated on the development of four algorithms: optic nerve head segmentation, lesion segmentation, image quality assessment and vessel width measurements. This research amalgamated these four algorithms with two existing techniques to form an integrated diagnostic system. The diagnostic system when used as a 'pre-filtering' tool successfully reduced the number of images requiring human grading by 74.3%: this was achieved by identifying and excluding images without sight threatening maculopathy from manual screening

Comparison of super-resolution algorithms applied to retinal images

A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations

Retinal image quality assessment using deep convolutional neural networks

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)Diabetic Retinopathy (DR) and diabetic macular edema (DME) are the damages caused to the retina and are complications that can affect the diabetic population. Diabetic retinopathy (DR), is the most common disease due to the presence of exudates and has three levels of severity, such as mild, moderate and severe, depending on the exudates distribution in the retina. For screening of diabetic retinopathy or a population-based clinical study, a large number of digital fundus images are captured and to be possible to recognize the signs of DR and DME, it is necessary that the images have quality, because low-quality images may force the patient to return for a second examination, wasting time and possibly delaying treatment. These images are evaluated by trained human experts, which can be a time-consuming and expensive task due to the number of images that need to be examined. Therefore, this is a field that would be hugely benefited with the development of an automated eye fundus quality assessment and analysis systems. It can potentially facilitate health care in remote regions and in developing countries where reading skills are scarce. Deep Learning is a kind of Machine Learning method that involves learning multi-level representations that begin with raw data entry and gradually moves to more abstract levels through non-linear transformations. With enough training data and sufficiently deep architectures, neural networks, such as Convolutional Neural Networks (CNN), can learn very complex functions and discover complex structures in the data. Thus, Deep Learning emerges as a powerful tool for medical image analysis and evaluation of retinal image quality using computer-aided diagnosis. Therefore, the aim of this study is to automatically assess all the three quality parameters alone (focus, illumination and color), and then an overall quality of fundus images assessment, classifying the images into the classes “accept” or “reject with a Deep Learning approach using convolutional neural networks (CNN). For the overall classification, the following results were obtained: test accuracy=97.89%, SN=97.9%, AUC=0.98 and 1-score=97.91%.A retinopatia diabética (RD) e o edema macular diabético (EMD) são patologias da retina e são uma complicação que pode afetar a população diabética. A retinopatia diabética é a doença mais comum devido à presença de exsudatos e possui três níveis de gravidade, como leve, moderado e grave, dependendo da distribuição dos exsudatos na retina. Para triagem da retinopatia diabética ou estudo clínico de base populacional, um grande número de imagens digitais de fundo do olho são capturadas e para ser possível reconhecer os sinais da RD e EMD, é necessário que as imagens tenham qualidade, pois imagens de baixa qualidade podem forçar o paciente a retornar para um segundo exame, perdendo tempo e, possivelmente, retardando o tratamento. Essas imagens são avaliadas por especialistas humanos treinados, o que pode ser uma tarefa demorada e cara devido ao número de imagens que precisam de ser examinadas. Portanto, este é um campo que seria enormemente beneficiado com o desenvolvimento de sistemas automatizados de avaliação e análise da qualidade da imagem do fundo de olho. Pode potencialmente facilitar a assistência médica em regiões remotas e em países em desenvolvimento, onde as habilidades de leitura são escassas. Deep Learning é um tipo de método de Machine Learning que envolve a aprendizagem de representações em vários níveis que começam com a entrada de dados brutos e gradualmente se transformam para níveis mais abstratos através de transformações não lineares, para se obterem as previsões. Com dados de treino suficientes e arquiteturas suficientemente profundas, as redes neuronais, como as Convolutional Neural Networks (CNN), podem aprender funções muito complexas e descobrir estruturas complexas nos dados. Assim, o Deep Learning surge como uma ferramenta poderosa para analisar imagens médicas para avaliação da qualidade da retina, usando diagnóstico auxiliado por computador a partir do fundo do olho. Portanto, o objetivo deste estudo é avaliar automaticamente a qualidade geral das imagens do fundo, classificando as imagens em “aceites” ou “rejeitadas”, com base em três parâmetros principais, como o foco, a iluminação e cor com abordagem de Deep Learning usando convolutional neural networks (CNN). Para a classificação geral da qualidade das imagens, obtiveram-se os seguintes resultados: acurácia do teste = 97,89%, SN = 97,9%, AUC = 0,98 e 1-score=97.91%

The Estimates of Retinal Ganglion Cell Counts Performed Better than Isolated Structure and Functional Tests for Glaucoma Diagnosis

Purpose. To evaluate the diagnostic accuracy of retinal ganglion cell (RGC) counts as estimated by combining data from standard automated perimetry (SAP) and spectral domain optical coherence tomography (SD-OCT). Methods. Healthy individuals and glaucoma patients were included in this cross-sectional study. All eyes underwent 24-2 SITA SAP and structural imaging tests. RGC count estimates were obtained using a previously described algorithm, which combines estimates of RGC numbers from SAP sensitivity thresholds and SD-OCT retinal nerve fiber layer (RNFL) average thickness. Results. A total of 119 eyes were evaluated, including 75 eyes of 48 healthy individuals and 44 eyes of 29 glaucoma patients. RGC count estimates performed better than data derived from SD-OCT RNFL average thickness or SAP mean deviation alone (area under ROC curves: 0.98, 0.92, and 0.79; P<0.001) for discriminating healthy from glaucomatous eyes, even in a subgroup of eyes with mild disease (0.97, 0.88, and 0.75; P<0.001). There was a strong and significant correlation between estimates of RGC numbers derived from SAP and SD-OCT (R2=0.74; P<0.001). Conclusion. RGC count estimates obtained by combined structural and functional data showed excellent diagnostic accuracy for discriminating the healthy from the glaucomatous eyes and performed better than isolated structural and functional parameters

Pupillary Responses to High-Irradiance Blue Light Correlate with Glaucoma Severity

PurposeTo evaluate whether a chromatic pupillometry test can be used to detect impaired function of intrinsically photosensitive retinal ganglion cells (ipRGCs) in patients with primary open-angle glaucoma (POAG) and to determine if pupillary responses correlate with optic nerve damage and visual loss.DesignCross-sectional study.ParticipantsOne hundred sixty-one healthy controls recruited from a community polyclinic (55 men; 151 ethnic Chinese) and 40 POAG patients recruited from a glaucoma clinic (22 men; 35 ethnic Chinese) 50 years of age or older.MethodsSubjects underwent monocular exposure to narrowband blue light (469 nm) or red light (631 nm) using a modified Ganzfeld dome. Each light stimulus was increased gradually over 2 minutes to activate sequentially the rods, cones, and ipRGCs that mediate the pupillary light reflex. Pupil diameter was recorded using an infrared pupillography system.Main Outcome MeasuresPupillary responses to blue light and red light were compared between control subjects and those with POAG by constructing dose-response curves across a wide range of corneal irradiances (7–14 log photons/cm2 per second). In patients with POAG, pupillary responses were evaluated relative to standard automated perimetry testing (Humphrey Visual Field [HVF]; Carl Zeiss Meditec, Dublin, CA) and scanning laser ophthalmoscopy parameters (Heidelberg Retinal Tomography [HRT]; Heidelberg Engineering, Heidelberg, Germany).ResultsThe pupillary light reflex was reduced in patients with POAG only at higher irradiance levels, corresponding to the range of activation of ipRGCs. Pupillary responses to high-irradiance blue light associated more strongly with disease severity compared with responses to red light, with a significant linear correlation observed between pupil diameter and HVF mean deviation (r = −0.44; P = 0.005) as well as HRT linear cup-to-disc ratio (r = 0.61; P < 0.001) and several other optic nerve head parameters.ConclusionsIn glaucomatous eyes, reduced pupillary responses to high-irradiance blue light were associated with greater visual field loss and optic disc cupping. In POAG, a short chromatic pupillometry test that evaluates the function of ipRGCs can be used to estimate the degree of damage to retinal ganglion cells that mediate image-forming vision. This approach could prove useful in detecting glaucoma

Optical Coherence Tomography Angiography (OCTA) in Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorder

Vascular changes are increasingly recognized as important factors in the pathophysiology of neuroinflammatory disease, especially in multiple sclerosis (MS). The relatively novel technology of optical coherence tomography angiography (OCTA) images the retinal and choroidal vasculature non-invasively and in a depth-resolved manner. OCTA provides an alternative quantitative measure of retinal damage, by measuring vascular density instead of structural atrophy. Preliminary results suggest OCTA is sensitive to retinal damage in early disease stages, while also having less of a "floor-effect" compared with commonly used OCT metrics, meaning it can pick up further damage in a severely atrophied retina in later stages of disease. Furthermore, it may serve as a surrogate marker for vascular pathology in the central nervous system. Data to date consistently reveal lower densities of the retinal microvasculature in both MS and neuromyelitis optica spectrum disorder (NMOSD) compared with healthy controls, even in the absence of prior optic neuritis. Exploring the timing of vascular changes relative to structural atrophy may help answer important questions about the role of hypoperfusion in the pathophysiology of neuroinflammatory disease. Finally, qualitative characteristics of retinal microvasculature may help discriminate between different neuroinflammatory disorders. There are however still issues regarding image quality and development of standardized analysis methods before OCTA can be fully incorporated into clinical practice

Automated Detection of Malarial Retinopathy in Digital Fundus Images for Improved Diagnosis in Malawian Children with Clinically Defined Cerebral Malaria

Cerebral malaria (CM), a complication of malaria infection, is the cause of the majority of malaria-associated deaths in African children. The standard clinical case definition for CM misclassifies ~25% of patients, but when malarial retinopathy (MR) is added to the clinical case definition, the specificity improves from 61% to 95%. Ocular fundoscopy requires expensive equipment and technical expertise not often available in malaria endemic settings, so we developed an automated software system to analyze retinal color images for MR lesions: retinal whitening, vessel discoloration, and white-centered hemorrhages. The individual lesion detection algorithms were combined using a partial least square classifier to determine the presence or absence of MR. We used a retrospective retinal image dataset of 86 pediatric patients with clinically defined CM (70 with MR and 16 without) to evaluate the algorithm performance. Our goal was to reduce the false positive rate of CM diagnosis, and so the algorithms were tuned at high specificity. This yielded sensitivity/specificity of 95%/100% for the detection of MR overall, and 65%/94% for retinal whitening, 62%/100% for vessel discoloration, and 73%/96% for hemorrhages. This automated system for detecting MR using retinal color images has the potential to improve the accuracy of CM diagnosis

Deep learning-based improvement for the outcomes of glaucoma clinical trials

Glaucoma is the leading cause of irreversible blindness worldwide. It is a progressive optic neuropathy in which retinal ganglion cell (RGC) axon loss, probably as a consequence of damage at the optic disc, causes a loss of vision, predominantly affecting the mid-peripheral visual field (VF). Glaucoma results in a decrease in vision-related quality of life and, therefore, early detection and evaluation of disease progression rates is crucial in order to assess the risk of functional impairment and to establish sound treatment strategies. The aim of my research is to improve glaucoma diagnosis by enhancing state of the art analyses of glaucoma clinical trial outcomes using advanced analytical methods. This knowledge would also help better design and analyse clinical trials, providing evidence for re-evaluating existing medications, facilitating diagnosis and suggesting novel disease management. To facilitate my objective methodology, this thesis provides the following contributions: (i) I developed deep learning-based super-resolution (SR) techniques for optical coherence tomography (OCT) image enhancement and demonstrated that using super-resolved images improves the statistical power of clinical trials, (ii) I developed a deep learning algorithm for segmentation of retinal OCT images, showing that the methodology consistently produces more accurate segmentations than state-of-the-art networks, (iii) I developed a deep learning framework for refining the relationship between structural and functional measurements and demonstrated that the mapping is significantly improved over previous techniques, iv) I developed a probabilistic method and demonstrated that glaucomatous disc haemorrhages are influenced by a possible systemic factor that makes both eyes bleed simultaneously. v) I recalculated VF slopes, using the retinal never fiber layer thickness (RNFLT) from the super-resolved OCT as a Bayesian prior and demonstrated that use of VF rates with the Bayesian prior as the outcome measure leads to a reduction in the sample size required to distinguish treatment arms in a clinical trial