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

Screening, Diagnosis, and Management of Open Angle Glaucoma: An Evidence-Based Guideline for Canadian Optometrists

Glaucoma is the most common form of irreversible blindness in the world, and second only to cataract among all causes of blindness. There is still no universally agreed-upon definition of glaucoma, and as such, it remains a condition for which there are differing views on the classification of individuals within the continuum of suspicion through diagnosis. Regardless, there appears to be consensus that glaucoma refers to a group of diseases that manifest as a characteristic progressive optic neuropathy and retinal ganglion cell loss that eventually leads to a permanent loss of visual field. Glaucoma is a major public health issue because individuals are typically asymptomatic until end stages of the disease when the associated vision loss is significant and irreversible. Studies have shown that the prevalence of undetected glaucoma is as high as 50% even in high income areas including North America and Australia, increasing to 90% in middle and low income areas such as Asia and Africa. This is at least in part a result of inadequate screening tools and strategies to detect this asymptomatic disease: without more individuals accessing routine eye examinations, glaucoma will continue to go undetected. Vision loss from glaucoma imposes significant societal and economic burdens that increase with disease severity: the direct costs of vision loss from glaucoma exceed $300 million annually in Canada, and approach$2 billion across North America

Automatic extraction of retinal features to assist diagnosis of glaucoma disease

Glaucoma is a group of eye diseases that have common traits such as high eye pressure, damage to the Optic Nerve Head (ONH) and gradual vision loss. It affects the peripheral vision and eventually leads to blindness if left untreated. The current common methods of diagnosis of glaucoma are performed manually by the clinicians. Clinicians perform manual image operations such as change of contrast, zooming in zooming out etc to observe glaucoma related clinical indications. This type of diagnostic process is time consuming and subjective. With the advancement of image and vision computing, by automating steps in the diagnostic process, more patients can be screened and early treatment can be provided to prevent any or further loss of vision. The aim of this work is to develop a system called Glaucoma Detection Framework (GDF), which can automatically determine changes in retinal structures and imagebased pattern associated with glaucoma so as to assist the eye clinicians for glaucoma diagnosis in a timely and effective manner. In this work, several major contributions have been made towards the development of the automatic GDF consisting of the stages of preprocessing, optic disc and cup segmentation and regional image feature methods for classification between glaucoma and normal images. Firstly, in the preprocessing step, a retinal area detector based on superpixel classification model has been developed in order to automatically determine true retinal area from a Scanning Laser Ophthalmoscope (SLO) image. The retinal area detector can automatically extract artefacts out from the SLO image while preserving the computational effciency and avoiding over-segmentation of the artefacts. Localization of the ONH is one of the important steps towards the glaucoma analysis. A new weighted feature map approach has been proposed, which can enhance the region of ONH for accurate localization. For determining vasculature shift, which is one of glaucoma indications, we proposed the ONH cropped image based vasculature classification model to segment out the vasculature from the ONH cropped image. The ONH cropped image based vasculature classification model is developed in order to avoid misidentification of optic disc boundary and Peripapillary Atrophy (PPA) around the ONH of being a part of the vasculature area. Secondly, for automatic determination of optic disc and optic cup boundaries, a Point Edge Model (PEM), a Weighted Point Edge Model (WPEM) and a Region Classification Model (RCM) have been proposed. The RCM initially determines the optic disc region using the set of feature maps most suitable for the region classification whereas the PEM updates the contour using the force field of the feature maps with strong edge profile. The combination of PEM and RCM entitled Point Edge and Region Classification Model (PERCM) has significantly increased the accuracy of optic disc segmentation with respect to clinical annotations around optic disc. On the other hand, the WPEM determines the force field using the weighted feature maps calculated by the RCM for optic cup in order to enhance the optic cup region compared to rim area in the ONH. The combination of WPEM and RCM entitled Weighted Point Edge and Region Classification Model (WPERCM) can significantly enhance the accuracy of optic cup segmentation. Thirdly, this work proposes a Regional Image Features Model (RIFM) which can automatically perform classification between normal and glaucoma images on the basis of regional information. Different from the existing methods focusing on global features information only, our approach after optic disc localization and segmentation can automatically divide an image into five regions (i.e. optic disc or Optic Nerve Head (ONH) area, inferior (I), superior(S), nasal(N) and temporal(T)). These regions are usually used for diagnosis of glaucoma by clinicians through visual observation only. It then extracts image-based information such as textural, spatial and frequency based information so as to distinguish between normal and glaucoma images. The method provides a new way to identify glaucoma symptoms without determining any geometrical measurement associated with clinical indications glaucoma. Finally, we have accommodated clinical indications of glaucoma including the CDR, vasculature shift and neuroretinal rim loss with the RIFM classification and performed automatic classification between normal and glaucoma images. Since based on the clinical literature, no geometrical measurement is the guaranteed sign of glaucoma, the accommodation of the RIFM classification results with clinical indications of glaucoma can lead to more accurate classification between normal and glaucoma images. The proposed methods in this work have been tested against retinal image databases of 208 fundus images and 102 Scanning Laser Ophthalmoscope (SLO) images. These databases have been annotated by the clinicians around different anatomical structures associated with glaucoma as well as annotated with healthy or glaucomatous images. In fundus images, ONH cropped images have resolution varying from 300 to 900 whereas in SLO images, the resolution is 341 x 341. The accuracy of classification between normal and glaucoma images on fundus images and the SLO images is 94.93% and 98.03% respectively

Circumpapillary OCT-Focused Hybrid Learning for Glaucoma Grading Using Tailored Prototypical Neural Networks

Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis

Retinal characteristics of myopic eyes in a semi-rural UK population

All levels of myopia are associated with an increased risk of ocular diseases such as glaucoma, and retinal detachment. The prevalence of myopia is increasing at an alarming rate across the globe so an increase in ocular morbidity would be expected unless action is taken. The studies in this thesis were carried out to investigate how the retina, and optic nerve head change in appearance at different levels of myopia. Identification of signs indicating future progression would allow targeting of interventions to minimise the ultimate degree of myopia. This thesis describes four community-based studies investigating retinal appearance in myopic eyes. A mixture of retrospective cross-sectional and longitudinal assessments using previously obtained digital fundus images are presented, along with a prospective cross-sectional study of the peripheral retina. The participants had myopia ≤-0.50 D and were mainly of white European ethnicity. Crescent width was found to increase with both age and level of myopia. Those with inferior-temporally located crescents had the highest levels of myopia. Tilted discs were associated with higher levels of myopia and smaller optic discs. Upon longitudinal assessment of disc measures, the optic cup measures, and crescent width were found to increase. Peripheral retinal lesions were observed in 27 % of eyes. Pigmentary degeneration was the most frequently observed and was associated with increasing age and the widest crescents. White without pressure was found in 5.8 % of eyes and was associated with a higher magnitude of myopia. Static retinal vessel analysis showed no significant relationships between retinal vessel calibre summary measures and myopia, age, or optic nerve head measures. The position of the myopic crescent is a possible predictor of future myopic progression. Further longitudinal study is needed to investigate this. The vertical disc diameter remained constant justifying the use of the quotient of the maximum crescent width to vertical disc diameter to determine crescent width change without the need for magnification correction

Optic Nerve Head Image Analysis for Glaucoma Progression Detection

Glaucoma is a leading cause of visual disability across the world and when diagnosed the glaucoma patient will spend the rest of their life receiving treatment in managed clinical care. In the glaucoma clinic, retinal and optic nerve head (ONH) imaging can be used to help the clinician to manage patient treatment appropriately. By providing high resolution images of the optic nerve head structures and identifying changes therein related to disease onset and progression, an objective measure can be obtained as to how well or badly treatment is preventing further disease damage. This thesis contributes to the field of glaucoma progression detection by the analysis of clinical imaging data using confocal scanning laser tomography (CSLT). Primarily it is an investigation of how best to appraise and optimise current algorithms which aim to detect these glaucomatous structural changes in the optic nerve head. This is done by addressing how the performance of these methods can be best assessed in the absence of a gold standard for glaucomatous structural progression. Glaucoma expert assessment of photographs of the optic disc is the current clinical standard of assessing glaucomatous damage evident in the ONH. This is used in this thesis to act as a reference standard by which these algorithms can be compared. In addition, the statistical principles underpinning trend detection techniques are also investigated along with the performance of these techniques to detect trends in CSLT data in the presence of different types of measurement noise and image quality. A new computer model is developed and validated to simulate stable series of CSLT images, with realistic variability, which can be used to benchmark the false-positive rates of current and future progression algorithms. In conclusion, the main results reported in this thesis show that uncertainties involved in expert assessment of change in ONH photographs limits this as a reference standard for structural change in glaucoma. In addition, since stability in clinical datasets is uncertain, simulation using modelled series is shown to provide a new benchmark for comparing methods of progression detection

En face OCT imaging for the assessment of glaucoma

Glaucoma is a leading cause of irreversible vision loss globally, and demands early and accurate diagnosis. OCT has become a key investigative technique in glaucoma, and, although it provides invaluable clinical support, detection of early glaucoma remains imperfect. Recent OCT developments enabled direct assessment of retinal nerve fibre bundle (RNFB) reflectance in en face OCT images. The technique has considerable potential in the assessment of glaucoma, yet it has limited clinical usability due to an incomplete understanding of RNFB features in healthy and glaucoma eyes and the lack of accepted methods to identify reflectance defects. This thesis aimed to better understand characteristics of RNFB reflectance in en face OCT imaging and to develop objective methods to extract defects in this domain. Structural and functional measures of glaucoma changes were collected in eyes with established glaucoma and age-similar controls. Results showed that the healthy configuration of RNFB varies across the retina and between different eyes. We developed a method for automated and objective examination of reflectivity changes in en face images. This method considers individual anatomy and varying RNFB configuration, and found more abnormalities than previous approaches. Measures of en face reflectance and conventional retinal nerve fibre layer thickness were strongly related. The agreement between changes of reflectance and visual function was moderate-to-good, and both testing domains presented concordant abnormalities in all tested eyes. Following further minimisation of artefacts in en face images, direct use of reflectance analysis or its combination with perimetry appear viable and with significant potential for clinical examination of glaucoma

Suprathreshold Visual Function in Glaucoma

Glaucoma is the leading cause of irreversible blindness worldwide but the effect of glaucoma on patients’ vision under suprathreshold conditions relevant to their natural visual environment is poorly understood. This project aimed to investigate and further understand the effects of glaucoma on three aspects of suprathreshold vision; apparent contrast of suprathreshold stimuli, detection and discrimination of image blur and crowding of peripheral vision. Psychophysical methods were employed to assess these three visual functions by measuring contrast matches of Gabor stimuli, blur detection and discrimination thresholds of edge stimuli and crowding ratios of Vernier targets. These measures were obtained from glaucoma observers tested within and outside of visual field defects and the data compared with healthy controls. Contrast matching ratios were similar between glaucoma and healthy age similar controls despite sensitivity loss in the glaucoma group. Blur detection and discrimination thresholds were similar between glaucoma observers’ tested within and outside of visual field defects and age-similar controls, though thresholds were slightly elevated for high contrast stimuli in the glaucoma visual field defect group. Crowding ratios were similar between participants with glaucoma and healthy young controls. The results demonstrate that aspects of suprathreshold visual function can be maintained in early glaucoma despite sensitivity loss at threshold. The results provide empirical evidence as to the asymptomatic nature of the disease in its early stages. It appears that in early glaucoma, there may be compensatory mechanisms at work within the visual system under suprathreshold conditions that can overcome loss of sensitivity at threshold.The College of Optometrist