Structural evaluation in inherited retinal diseases.

Ophthalmic genetics is a field that has been rapidly evolving over the last decade, mainly due to the flourishing of translational medicine for inherited retinal diseases (IRD). In this review, we will address the different methods by which retinal structure can be objectively and accurately assessed in IRD. We review standard-of-care imaging for these patients: colour fundus photography, fundus autofluorescence imaging and optical coherence tomography (OCT), as well as higher-resolution and/or newer technologies including OCT angiography, adaptive optics imaging, fundus imaging using a range of wavelengths, magnetic resonance imaging, laser speckle flowgraphy and retinal oximetry, illustrating their utility using paradigm genotypes with on-going therapeutic efforts/trials

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans

A Foundation LAnguage-Image model of the Retina (FLAIR): Encoding expert knowledge in text supervision

Foundation vision-language models are currently transforming computer vision, and are on the rise in medical imaging fueled by their very promising generalization capabilities. However, the initial attempts to transfer this new paradigm to medical imaging have shown less impressive performances than those observed in other domains, due to the significant domain shift and the complex, expert domain knowledge inherent to medical-imaging tasks. Motivated by the need for domain-expert foundation models, we present FLAIR, a pre-trained vision-language model for universal retinal fundus image understanding. To this end, we compiled 37 open-access, mostly categorical fundus imaging datasets from various sources, with up to 97 different target conditions and 284,660 images. We integrate the expert's domain knowledge in the form of descriptive textual prompts, during both pre-training and zero-shot inference, enhancing the less-informative categorical supervision of the data. Such a textual expert's knowledge, which we compiled from the relevant clinical literature and community standards, describes the fine-grained features of the pathologies as well as the hierarchies and dependencies between them. We report comprehensive evaluations, which illustrate the benefit of integrating expert knowledge and the strong generalization capabilities of FLAIR under difficult scenarios with domain shifts or unseen categories. When adapted with a lightweight linear probe, FLAIR outperforms fully-trained, dataset-focused models, more so in the few-shot regimes. Interestingly, FLAIR outperforms by a large margin more generalist, larger-scale image-language models, which emphasizes the potential of embedding experts' domain knowledge and the limitations of generalist models in medical imaging.Comment: The pre-trained model is available at: https://github.com/jusiro/FLAI

Adaptive optics: principles and applications in ophthalmology

This is a comprehensive review of the principles and applications of adaptive optics (AO) in ophthalmology. It has been combined with flood illumination ophthalmoscopy, scanning laser ophthalmoscopy, as well as optical coherence tomography to image photoreceptors, retinal pigment epithelium (RPE), retinal ganglion cells, lamina cribrosa and the retinal vasculature. In this review, we highlight the clinical studies that have utilised AO to understand disease mechanisms. However, there are some limitations to using AO in a clinical setting including the cost of running an AO imaging service, the time needed to scan patients, the lack of normative databases and the very small size of area imaged. However, it is undoubtedly an exceptional research tool that enables visualisation of the retina at a cellular level

Choroideremia: from genetic and clinical phenotyping to gene therapy and future treatments

Choroideremia is an X-linked inherited chorioretinal dystrophy leading to blindness by late adulthood. Choroideremia is caused by mutations in the CHM gene which encodes Rab escort protein 1 (REP1), an ubiquitously expressed protein involved in intracellular trafficking and prenylation activity. The exact site of pathogenesis remains unclear but results in degeneration of the photoreceptors, retinal pigment epithelium and choroid. Animal and stem cell models have been used to study the molecular defects in choroideremia and test effectiveness of treatment interventions. Natural history studies of choroideremia have provided additional insight into the clinical phenotype of the condition and prepared the way for clinical trials aiming to investigate the safety and efficacy of suitable therapies. In this review, we provide a summary of the current knowledge on the genetics, pathophysiology, clinical features and therapeutic strategies that might become available for choroideremia in the future, including gene therapy, stem cell treatment and small-molecule drugs with nonsense suppression action

Characterizing Stargardt disease-causing mutations to identify ABCA4 gene lesions amenable to splice intervention therapeutics

Stargardt disease (STGD1, OMIM: 248200) is an autosomal recessive retinal dystrophy, characterized by bilateral progressive central vision loss and subretinal deposition of lipofuscin-like substances. The wide spectrum of clinical phenotypes, ranging from childhood-onset cone-rod dystrophy to late-onset macular pattern dystrophy-like disease, indicates a more complex genotype-phenotype correlation than previously believed. The association of mutations in the ATP-binding cassette transporter gene, ABCA4, with STGD1 was first reported in two families in 1997. The ABCA4 protein encoded by ABCA4 is predominantly expressed in outer segments of photoreceptors and retinal pigment epithelial (RPE) cells in retina..

Extracting spacing-derived estimates of rod density in healthy retinae

Quantification of the rod photoreceptor mosaic using adaptive optics scanning light ophthalmoscopy (AOSLO) remains challenging. Here we demonstrate a method for deriving estimates of rod density and rod:cone ratio based on measures of rod spacing, cone numerosity, and cone inner segment area. Twenty-two AOSLO images with complete rod visualization were used to validate this spacing-derived method for estimating density. The method was then used to estimate rod metrics in an additional 105 images without complete rod visualization. The spacing-derived rod mosaic metrics were comparable to published data from histology. This method could be leveraged to develop large normative databases of rod mosaic metrics, though limitations persist with intergrader variability in assessing cone area and numerosity

Investigation of the genetic cause and related phenotypes of rare early onset retinal dystrophies

Early onset retinal dystrophies (EORD) are a group of disorders presenting in childhood with degenerative abnormalities in photoreceptor cells. They are one of the leading causes of sight impairment in the United Kingdom. Since the initial discovery of Rho causing dominant retinitis pigmentosa in 1990, more than 160 genes have been associated with retinal dystrophy. Many, including CRB1, CRX, and RPE65 exhibit phenotypic heterogeneity and have been associated with more than one retinal disorder. Increasingly, with the advent of next generation sequencing, the association of non-syndromic retinal dystrophy with mutations in syndromic genes has been reported including CEP290, CLN3, and BBS1. In this thesis, a large cohort of patients with EORD underwent both detailed phenotyping to characterise their condition and molecular genetic investigations to identify and investigate the underlying causative variants. Many areas of the presented research were driven by novel findings on whole-exome sequencing such as the association of IFT140 with non-syndromic retinal dystrophy or CRX with macular dystrophy. Other areas were driven by unusual groups of patients with limited published data on their condition such as COL18A1 and Knobloch syndrome, with novel phenotypic features of cone-rod dysfunction and pigmentary glaucoma. Sanger sequencing was performed for confirmation and segregation of identified variants but in addition, for investigation of phenotypically similar patient panels for unusual gene associations. This included systemically mild Hermansky-Pudlak syndrome due to HPS6, juvenile macular dystrophy and CDH3, macular dystrophy and CRX and microcephaly with familial exudative vitreoretinopathy due to LRP5. Functional investigation of missense variants in IFT140 related retinal dystrophy was performed with transient cell transfection. This thesis highlights the vast heterogeneity of rare forms of EORD, presents novel clinical and molecular data and describes the key features of conditions to aid diagnosis and opportunities for future research

The Monogenic Architecture of Retinal and Neurological Diseases

Monogenic diseases, or single-gene disorders, are clinical manifestations that can be traced to genetic variation in a single gene that alters the biologically intended (wildtype) function of its protein (or mRNA) product. Although the causal gene and its function are well-understood in many monogenic diseases, this knowledge alone often does not fully encapsulate the extensive clinical spectrum of phenotypes seen in patients. This is due in part to the numerous types of pathogenic variants that can arise in a single gene, all of which can have distinct effects on disease expression. Understanding the relationship between the vast number of possible genotypes and corresponding disease phenotypes defines a gene’s monogenic disease architecture—an important but poorly understood concept that can yield informative mechanistic and clinical insight. This doctoral dissertation integrates traditional sequencing approaches with in-depth characterization of patient phenotypes to elucidate the monogenic disease architecture of three etiologically distinct disorders: retinal degeneration caused by autosomal recessive variation in ABCA4 and neurodevelopmental disease entities caused by autosomal dominant variants in CERT1 and PUM1. Genetic modifiers are identified as a significant factor in the penetrance of the major disease-causing allele of ABCA4 and several other genetic inconsistencies are resolved to create a coherent genotype-phenotype model for the disease. Insight from this model is then applied to demonstrate the effect of allele differences in disease progression and evaluation of treatment efficacy in patients. A large cohort of affected individuals with CERT1 variation is assembled to (1) validate the causal role of CERT1 in disease, (2) delineate the precise mechanism of CERT protein dysfunction in sphingolipid metabolism and (3) demonstrate therapeutic efficacy of an inhibitor compound for a newly described syndrome. Finally, the mutational spectrum of PUM1 is expanded to previously unattributed variant classes with unexpected pathophysiological consequences to patients. Not only do the findings in this dissertation advance the prospects of delivering personalized, precision medicine to patients, the overall impact underscores the importance of this integrated approach in reconciling knowledge gaps between observations at the molecular and organismal level