Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications

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

Advances in Ophthalmology

This book focuses on the different aspects of ophthalmology - the medical science of diagnosis and treatment of eye disorders. Ophthalmology is divided into various clinical subspecialties, such as cornea, cataract, glaucoma, uveitis, retina, neuro-ophthalmology, pediatric ophthalmology, oncology, pathology, and oculoplastics. This book incorporates new developments as well as future perspectives in ophthalmology and is a balanced product between covering a wide range of diseases and expedited publication. It is intended to be the appetizer for other books to follow. Ophthalmologists, researchers, specialists, trainees, and general practitioners with an interest in ophthalmology will find this book interesting and useful

Predicting risk of cardiovascular disease using retinal OCT imaging

We investigated the potential of optical coherence tomography (OCT) as an additional imaging technique to predict future cardiovascular disease (CVD). We utilised a self-supervised deep learning approach based on Variational Autoencoders (VAE) to learn low-dimensional representations of high-dimensional 3D OCT images and to capture distinct characteristics of different retinal layers within the OCT image. A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases. Our predictive model, trained on multimodal data, was assessed based on its ability to correctly identify individuals likely to suffer from a CVD event(MI or stroke), within a 5-year interval after image acquisition. Our self-supervised VAE feature selection and multimodal Random Forest classifier differentiate between patients at risk of future CVD events and the control group with an AUC of 0.75, outperforming the clinically established QRISK3 score (AUC= 0.597). The choroidal layer visible in OCT images was identified as an important predictor of future CVD events using a novel approach to model explanability. Retinal OCT imaging provides a cost-effective and non-invasive alternative to predict the risk of cardiovascular disease and is readily accessible in optometry practices and hospitals

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Functional outcome of retinal oedema and its standard treatment

Macular oedema is a pathological condition of fluid accumulation in the retinal tissues. It is a nonspecific sign of several retinal diseases that in the long term can lead to permanent vision loss. The clinical aspect of macular oedema treatment and vision recovery is reduction of the amount of fluid accumulated in the retina. Due to its complex pathophysiological mechanism, macular oedema has proven challenging to manage. Many unanswered questions remain in the ophthalmology world on this subject. The development of recent diagnostic tools such as optical coherence tomography allows better understanding of the morphological changes in the retina. Now we are able to detect retinal oedema and characterise it by location, depth, and amount of fluid. Further, clinicians are now able to assess therapeutic response by examining the anatomical structures of the retina. Yet, with techniques offering objective accuracy, emerging reports have shown discrepancies between clinically examined visual acuity, anatomical changes of the retina, and patients’ self-reported visual ability. The presence of such discrepancies is also supported by the fact that results achieved by randomised clinical trials rarely align with results attained in real-world settings. Today, functional vision testing can be performed with several different methods including questionnaires, colour vision tests, reading speed tests, contrast sensitivity tests etc. Nevertheless, none of these methods are widely used in clinical settings, and their predictive capabilities have yet to be explored. Establishing precise methodology for functional vision testing is likely to provide better understanding of patients’ treatment response. This thesis aims to investigate the potential predictive capabilities of functional vision tests and to compare these capabilities with those of well-established, routine ophthalmic examinations such as visual acuity and retinal thickness tests. In the current research, I focused on the following functional examinations: the visual function questionnaire (VFQ- 25), reading speed testing, and testing of the contrast sensitivity of the macula area (examined by microperimetry). These techniques allowed very specific and sensitive testing of the functionality of the retina. In addition, I explored functional vision tests and their association to the routine ophthalmic tests and their ability to detect sub-clinical changes in vision. I believe further research in this area will offer better understanding of the functional vision changes in patients with macular oedema and potentially will help in improving visionrelated quality of life

Advancing treatment of retinal disease through in silico trials

Abstract Treating retinal diseases to prevent sight loss is an increasingly important challenge. Thanks to the configuration of the eye, the retina can be examined relatively easily in situ. Owing to recent technological development in scanning devices, much progress has been made in understanding the structure of the retina and characterising retinal biomarkers. However, treatment options remain limited and are often of low efficiency and efficacy.&amp;#xD;&amp;#xD;In recent years, the concept of in silico clinical trials has been adopted by many pharmaceutical companies to optimise and accelerate the development of therapeutics. In silico clinical trials rely on the use of mathematical models based on the physical and biochemical mechanisms underpinning a biological system. With appropriate simplifications and assumptions, one can generate computer simulations of various treatment regimens, new therapeutic molecules, delivery strategies and so forth, rapidly and at a fraction of the cost required for the equivalent experiments. Such simulations have the potential not only to hasten the development of therapies and strategies but also to&amp;#xD;optimise the use of existing therapeutics.&amp;#xD;&amp;#xD;In this paper, we review the state-of-the-art in in silico models of the retina for mathematicians, biomedical scientists and clinicians, highlighting the challenges to developing in silico clinical trials. Throughout this paper, we highlight key findings from in silico models about the physiology of the retina in health and disease. We describe the main building blocks of in silico clinical trials and identify challenges to developing in silico clinical trials of retinal diseases.</jats:p