Diabetic Retinopathy Classification and Interpretation using Deep Learning Techniques

La retinopatia diabètica és una malaltia crònica i una de les principals causes de ceguesa i discapacitat visual en els pacients diabètics. L'examen ocular a través d'imatges de la retina és utilitzat pels metges per detectar les lesions relacionades amb aquesta malaltia. En aquesta tesi, explorem diferents mètodes innovadors per a la classificació automàtica del grau de malaltia utilitzant imatges del fons d'ull. Per a aquest propòsit, explorem mètodes basats en l'extracció i classificació automàtica, basades en xarxes neuronals profundes. A més, dissenyem un nou mètode per a la interpretació dels resultats. El model està concebut de manera modular per a que pugui ser utilitzat en d'altres xarxes i dominis de classificació. Demostrem experimentalment que el nostre model d'interpretació és capaç de detectar lesions de retina a la imatge únicament a partir de la informació de classificació. A més, proposem un mètode per comprimir la representació interna de la informació de la xarxa. El mètode es basa en una anàlisi de components independents sobre la informació del vector d'atributs intern de la xarxa generat pel model per a cada imatge. Usant el nostre mètode d'interpretació esmentat anteriorment també és possible visualitzar aquests components en la imatge. Finalment, presentem una aplicació experimental del nostre millor model per classificar imatges de retina d'una població diferent, concretament de l'Hospital de Reus. Els mètodes proposats arriben al nivell de rendiment de l'oftalmòleg i són capaços d'identificar amb gran detall les lesions presents en les imatges, que es dedueixen només de la informació de classificació de la imatge.La retinopatía diabética es una enfermedad crónica y una de las principales causas de ceguera y discapacidad visual en los pacientes diabéticos. El examen ocular a través de imágenes de la retina es utilizado por los médicos para detectar las lesiones relacionadas con esta enfermedad. En esta tesis, exploramos diferentes métodos novedosos para la clasificación automática del grado de enfermedad utilizando imágenes del fondo de la retina. Para este propósito, exploramos métodos basados en la extracción y clasificación automática, basadas en redes neuronales profundas. Además, diseñamos un nuevo método para la interpretación de los resultados. El modelo está concebido de manera modular para que pueda ser utilizado utilizando otras redes y dominios de clasificación. Demostramos experimentalmente que nuestro modelo de interpretación es capaz de detectar lesiones de retina en la imagen únicamente a partir de la información de clasificación. Además, proponemos un método para comprimir la representación interna de la información de la red. El método se basa en un análisis de componentes independientes sobre la información del vector de atributos interno de la red generado por el modelo para cada imagen. Usando nuestro método de interpretación mencionado anteriormente también es posible visualizar dichos componentes en la imagen. Finalmente, presentamos una aplicación experimental de nuestro mejor modelo para clasificar imágenes de retina de una población diferente, concretamente del Hospital de Reus. Los métodos propuestos alcanzan el nivel de rendimiento del oftalmólogo y son capaces de identificar con gran detalle las lesiones presentes en las imágenes, que se deducen solo de la información de clasificación de la imagen.Diabetic Retinopathy is a chronic disease and one of the main causes of blindness and visual impairment for diabetic patients. Eye screening through retinal images is used by physicians to detect the lesions related with this disease. In this thesis, we explore different novel methods for the automatic diabetic retinopathy disease grade classification using retina fundus images. For this purpose, we explore methods based in automatic feature extraction and classification, based on deep neural networks. Furthermore, as results reported by these models are difficult to interpret, we design a new method for results interpretation. The model is designed in a modular manner in order to generalize its possible application to other networks and classification domains. We experimentally demonstrate that our interpretation model is able to detect retina lesions in the image solely from the classification information. Additionally, we propose a method for compressing model feature-space information. The method is based on a independent component analysis over the disentangled feature space information generated by the model for each image and serves also for identifying the mathematically independent elements causing the disease. Using our previously mentioned interpretation method is also possible to visualize such components on the image. Finally, we present an experimental application of our best model for classifying retina images of a different population, concretely from the Hospital de Reus. The methods proposed, achieve ophthalmologist performance level and are able to identify with great detail lesions present on images, inferred only from image classification information

The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases

: In recent years, the role of artificial intelligence (AI) and deep learning (DL) models is attracting increasing global interest in the field of ophthalmology. DL models are considered the current state-of-art among the AI technologies. In fact, DL systems have the capability to recognize, quantify and describe pathological clinical features. Their role is currently being investigated for the early diagnosis and management of several retinal diseases and glaucoma. The application of DL models to fundus photographs, visual fields and optical coherence tomography (OCT) imaging has provided promising results in the early detection of diabetic retinopathy (DR), wet age-related macular degeneration (w-AMD), retinopathy of prematurity (ROP) and glaucoma. In this review we analyze the current evidence of AI applied to these ocular diseases, as well as discuss the possible future developments and potential clinical implications, without neglecting the present limitations and challenges in order to adopt AI and DL models as powerful tools in the everyday routine clinical practice

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

CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology

Biomedical Applications of Mid-Infrared Spectroscopic Imaging and Multivariate Data Analysis: Contribution to the Understanding of Diabetes Pathogenesis

Diabetic retinopathy (DR) is a microvascular complication of diabetes and a leading cause of adult vision loss. Although a great deal of progress has been made in ophthalmological examinations and clinical approaches to detect the signs of retinopathy in patients with diabetes, there still remain outstanding questions regarding the molecular and biochemical changes involved. To discover the biochemical mechanisms underlying the development and progression of changes in the retina as a result of diabetes, a more comprehensive understanding of the bio-molecular processes, in individual retinal cells subjected to hyperglycemia, is required. Animal models provide a suitable resource for temporal detection of the underlying pathophysiological and biochemical changes associated with DR, which is not fully attainable in human studies. In the present study, I aimed to determine the nature of diabetes-induced, highly localized biochemical changes in the retinal tissue from Ins2Akita/+ (Akita/+; a model of Type I diabetes) male mice with different duration of diabetes. Employing label-free, spatially resolved Fourier transform infrared (FT-IR) imaging engaged with chemometric tools enabled me to identify temporal-dependent reproducible biomarkers of the diabetic retinal tissue from mice with 6 or 12 weeks, and 6 or 10 months of diabetes. I report, for the first time, the origin of molecular changes in the biochemistry of individual retinal layers with different duration of diabetes. A robust classification between distinctive retinal layers - namely photoreceptor layer (PRL), outer plexiform layer (OPL), inner nuclear layer (INL), and inner plexiform layer (IPL) - and associated temporal-dependent spectral biomarkers, were delineated. Spatially-resolved super resolution chemical images revealed oxidative stress-induced structural and morphological alterations within the nucleus of the photoreceptors. Comparison among the PRL, OPL, INL, and IPL suggested that the photoreceptor layer is the most susceptible layer to the oxidative stress with short-duration of diabetes. Moreover, for the first time, we present the temporal-dependent molecular alterations for the PRL, OPL, INL, and IPL from Akita/+ mice, with progression of diabetes. These findings are potentially important and may be of particular benefit in understanding the molecular and biological activity of retinal cells during oxidative stress in diabetes. Our integrating paradigm provides a new conceptual framework and a significant rationale for a better understanding of the molecular and cellular mechanisms underlying the development and progression of DR. This approach may yield alternative and potentially complimentary methods for the assessment of diabetes changes. It is expected that the conclusions drawn from this work will bridge the gap in our knowledge regarding the biochemical mechanisms of the DR and address some critical needs in the biomedical community

A novel few-shot classification framework for diabetic retinopathy detection and grading

Diabetes Retinopathy (DR) is a major microvascular complication of diabetes. Computer-Aided Diagnosis (CAD) tools for DR management are primarily developed using Artificial Intelligence (AI) methods, such as machine and deep learning algorithms. DR diagnostic tools have been developed in recent years using deep learning models. Thus, these models require large amounts of data for training. Consequently, these huge amounts of data are not balanced due to fewer cases in the dataset. To solve the problems associated with training models with small datasets, such as overfitting and poor approximation, this paper proposes a paradigm called Few-Shot Learning (FSL) which uses a relatively small amount of training data to train the models effectively. This paper proposes a novel prototype network, a type of FSL classification network capable of grading and detecting DR based on attention. The DRNet framework uses episodic learning to train its model on few-shot classification tasks. We developed a DRNet based on the APTOS2019 dataset for diabetic detection and grading. In the proposed network, aggregated transformations and gradient activations of classes are leveraged to design the attention mechanism to capture image representations. As a result, the system achieves 99.73 % accuracy, 99.82 % sensitivity, 99.63 % specificity in DR detection, 98.18 % accuracy, 97.41% sensitivity, and 99.55% specificity in DR grading. An analysis of objective performance metrics and model interpretation shows that the proposed model can detect DR more efficiently and grade the severity more accurately when using unseen fundus images than existing state-of-the-art methods. Therefore, this tool could help provide a second opinion to an ophthalmologist about the severity level of DR