Wide-Field Retinal Imaging in Adults and Children

Wide-field retinal imaging has become an important standard of care imaging modality in many retinal disorders both in adults and children. The recently developed wide-field retinal imaging systems enable approximately 200° imaging of retina. In this chapter, we would like to review the use of wide-field retinal imaging in disorders such as retinal vascular diseases, uveal and retinal inflammatory diseases, intraocular tumors, peripheral retinal pathologies, and retinal disorders in children such as retinopathy of prematurity, familial exudative vitreoretinopathy, and Coats\u27 disease. Also, we would like to address the rapidly expanding role of peripheral retinal imaging in treating systemic diseases. The use of wide-field imaging technologies in screening, diagnosis, treatment, and documentation of retinal pathologies and the new information provided by wide-field angiography for retinal vascular diseases and macular problems will be discussed

Retinal Imaging in Infants

Digital retinal imaging is at the core of a revolution that is continually improving the screening, diagnosis, documentation, monitoring, and treatment of infant retinal diseases. Historically, imaging the retina of infants had been limited and difficult to obtain. Recent advances in photographic instrumentation have significantly improved the ability to obtain high-quality multimodal images of the infant retina. These include color fundus photography with different camera angles, ultrasonography, fundus fluorescein angiography, optical coherence tomography, and optical coherence tomography angiography. We provide a summary of the current literature on retinal imaging in infants and highlight areas where further research is required

Current and future roles of artificial intelligence in retinopathy of prematurity

Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness. While semi-automated systems have been used in the past to diagnose ROP-related plus disease by quantifying retinal vessel features, traditional machine learning (ML) models face challenges like accuracy and overfitting. Recent advancements in deep learning (DL), especially convolutional neural networks (CNNs), have significantly improved ROP detection and classification. The i-ROP deep learning (i-ROP-DL) system also shows promise in detecting plus disease, offering reliable ROP diagnosis potential. This research comprehensively examines the contemporary progress and challenges associated with using retinal imaging and artificial intelligence (AI) to detect ROP, offering valuable insights that can guide further investigation in this domain. Based on 89 original studies in this field (out of 1487 studies that were comprehensively reviewed), we concluded that traditional methods for ROP diagnosis suffer from subjectivity and manual analysis, leading to inconsistent clinical decisions. AI holds great promise for improving ROP management. This review explores AI's potential in ROP detection, classification, diagnosis, and prognosis.Comment: 28 pages, 8 figures, 2 tables, 235 references, 1 supplementary tabl

Application of smartphone ophthalmoscope in ophthalmic clinical practice and teaching

The rapid development and increasing popularity of smartphones have led to their gradual integration as essential aids in medical examinations and teaching tools. The smartphone ophthalmoscope (SO) is one of them. Specifically, SO is comprised of a smartphone and an attachment. The smartphone serves as a versatile device for recording and transmitting information, while the attachment functions as a tool for phone fixation as well as focus adjustment and providing light resources, albeit with slight variations across different devices. Presently, the convenience, universality, and transferability of SO have greatly expanded its potential in fields of clinical practice and ocular teaching. The review provides a concise overview of the contemporary SO devices, elucidates the diseases amenable to assessment via SO, and outlines the various applications of SO in clinical practice and teaching

Using Novel Fundus Image Preprocessing to Improve the Classification of Retinopathy of Prematurity (ROP) Using Deep Learning

Retinopathy of Prematurity (ROP) can affect babies born prematurely. It is a potentially blinding eye disorder which can arise from the complications of undeveloped retina. Thus, screening of ROP is essential for early detection and treatment in these infants. An Retcam digital camera is used to capture patient’s retinal image. The captured image quality is constrained due to many factors. Effective and accurate image pre-processing methods for ROP Retcam images are required for improving ROP clinical features prior to being used in any CNN based classification system. We reviewed present literature on image pre-processing pertaining to digital retina images. This included image domain, restoration based methods and the latest machine learning methods. Our first contributions were two improved novel restoration image pre-processing methods. An image domain method was then applied to the output of these improved methods to create new hybrid methods. These new pre-processing methods improved ROP clinical features in ROP Retcam images. The third contribution used a novel approach using deep learning-based segmentation classifier to generate vessel map from an ROP Retcam image. The purpose was to erode the blood vessels from the original image thereby reducing the blood vessel noise. For our fourth contribution, we used transfer learning based CNNs, namely, InceptionResv2, and ResNet50 to create 3 sets of classifiers representing each ROP condition, namely Plus Disease, Stages and Zones. These CNNs were trained and validated using the improved pre-processing methods and traditional methods independently. The comparative evaluations of all identified pre-processing methods showed that these new pre-processing methods contributed to higher accuracy when classifying ROP using limited training images. With these methods, our results were as equal or better than comparative peer results using limited data. In this research, using the above components, we created a framework, McROP, that deals with key three ROP conditions. This framework can be extended easily to other pediatric ophthalmology conditions. To our knowledge, this is the first known use of restoration-based image pre-processing for ROP Retcam for improving ROP clinical features. These methods demonstrated effectiveness in CNNs based classification for ROP when compared against traditional pre-processing methods.ThesisDoctor of Philosophy (PhD

Review of smartphone funduscopy for diabetic retinopathy screening

This is the final version. Available on open access from Elsevier via the DOI in this record. I detail advances in funduscopy diagnostic systems integrating smartphones. Smartphone funduscopy devices are comprised of lens devices connecting with smartphones and software applications to be used for mobile retinal image capturing and diagnosis of diabetic retinopathy. This is particularly beneficial to automate and mobilize retinopathy screening techniques and methods in remote and rural areas as those diabetes patients are often not meeting the required regular screening for diabetic retinopathy. Smartphone retinal image grading systems enable retinopathy to be screened remotely as teleophthalmology or as a stand-alone point-of-care-testing system. Smartphone funduscopy aims to avoid the need for patients to be seen by expert ophthalmologists, which can reduce patient travel, time taken for images to be processed, appointment backlog, health service overhead costs, and the workload burden for expert ophthalmologists.Operating Budget, Research Englan

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

The use of smartphones in ophthalmology: technological development and application

Objective: Technological development has promoted several advances in society, including the creation of smartphones, which have been increasingly used in medicine, especially in ophthalmology. This study aimed to review the use of smartphones in ophthalmology. Methods: In January of 2020, the MEDLINE and LILACS databases were selected to provide articles containing the terms “Ophthalmology” and “Smartphone”, filtering the results between the years of 2015 and 2019. The evaluated outcomes were finally included into the following categories in the discussion: “Visual acuity”, “Amblyopia and strabismus”, “Anterior segment”, “Posterior segment”, “Glaucoma”, “Community patient education and assistance” and “Neurophthalmology”. Results: Smartphones can be useful in several different areas of ophthalmology and can provide the patients better understating and adhesion to their treatment. Conclusion: Applications can be used as tools to facilitate the work of several professionals and improve the understanding of patients about their clinical conditions

Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence

Background: An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. Aim: This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). Methods: The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. Results: Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. Conclusion: A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases