RDH12 retinopathy: clinical features, biology, genetics and future directions

Retinol dehydrogenase 12 (RDH12) is a small gene located on chromosome 14, encoding an enzyme capable of metabolizing retinoids. It is primarily located in photoreceptor inner segments and thereby is believed to have an important role in clearing excessive retinal and other toxic aldehydes produced by light exposure. Clinical features: RDH12-associated retinopathy has wide phenotypic variability; including early-onset severe retinal dystrophy/Leber Congenital Amaurosis (EOSRD/LCA; most frequent presentation), retinitis pigmentosa, cone-rod dystrophy, and macular dystrophy. It can be inherited in an autosomal recessive and dominant fashion. RDH12-EOSRD/LCA's key features are early visual impairment, petal-shaped, coloboma-like macular atrophy with variegated watercolour-like pattern, peripapillary sparing, and often dense bone spicule pigmentation. Future directions: There is currently no treatment available for RDH12-retinopathy. However, extensive preclinical investigations and an ongoing prospective natural history study are preparing the necessary foundation to design and establish forthcoming clinical trials. Herein, we will concisely review pathophysiology, molecular genetics, clinical features, and discuss therapeutic approaches

Prognostication in Stargardt disease using Fundus Autofluorescence: Improving Patient Care

PURPOSE: To explore fundus autofluorescence (FAF) imaging as an alternative to electroretinogram (ERG), as a non-invasive, quick, and readily interpretable method to predict disease progression in Stargardt disease (STGD). DESIGN: Retrospective case series of patients who attended Moorfields Eye Hospital (London, UK). SUBJECTS: Patients with STGD who met the following criteria were included: (i) biallelic disease-causing variants in ABCA4, (ii) ERG testing performed inhouse with an unequivocal ERG group classification, and (iii) ultra-widefield (UWF) FAF imaging performed up to 2 years before or after the ERG. METHODS: Patients were divided into three ERG groups based on retinal function and three FAF groups according to the extent of the hypoautofluorescence and their retinal background appearance. FAF imaging of 30 and 55° were also subsequently reviewed. MAIN OUTCOME MEASURES: ERG/FAF concordance and its association with baseline visual acuity and genetics. RESULTS: 234 patients were included in the cohort. 170 patients (73%) had the same ERG and FAF group, 33 (14%) had a milder FAF than ERG group, and 31 (13%) had a more severe FAF than ERG group. Children under the age of 10 (n=23) had the lowest ERG/FAF concordance, 57% (9 out of the 10 with discordant ERG/FAF had milder FAF than ERG), and adults with adult onset had the highest (80%). Missense genotypes were more commonly seen in the mildest phenotypes. In 97% and 98% of the cases, respectively, 30° and 55° FAF imaging matched with the group defined by UWF FAF. CONCLUSIONS: We demonstrate that FAF imaging is an effective modality to determine the extent of retinal involvement and thereby inform prognostication, by comparing FAF to the current gold standard of ERG testing to determine retinal involvement and thereby prognosis. In 80% of patients in our large molecularly proven cohort we were able to predict if the disease was confined to the macula or also affected the peripheral retina. Children assessed at a young age, with at least one null variant, early disease onset, and/or poor initial VA may have wider retinal involvement than predicted by FAF alone and/or progress to a more severe FAF phenotype over time

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

CRB1-associated Retinal Dystrophies: Genetics, Clinical Characteristics and Natural History

PURPOSE: To analyse the clinical characteristics, natural history, and genetics of CRB1-associated retinal dystrophies. DESIGN: Multicenter international retrospective cohort study. METHODS: Review of clinical notes, ophthalmic images, and genetic testing results of 104 patients (91 probands) with disease-causing CRB1 variants. Macular optical coherence tomography (OCT) parameters, visual function, fundus characteristics, and associations between variables were our main outcome measures. RESULTS: The mean age of the cohort at the first visit was 19.8 ± 16.1 (median 15) years of age, with a mean follow-up of 9.6 ± 10 years. Based on history, imaging, and clinical examination, 26 individuals were diagnosed with retinitis pigmentosa (RP, 26%), 54 with early-onset severe retinal dystrophy/Leber Congenital Amaurosis (EOSRD/LCA, 51%), and 24 with macular dystrophy (MD, 23%). Severe visual impairment was most frequent after 40 years of age for patients with RP and after 20 years of age for EOSRD/LCA. Longitudinal analysis revealed a significant difference between baseline and follow up best corrected visual acuity in the three sub-cohorts. Macular thickness decreased in most patients with EOSRD/LCA and MD, whereas the majority of patients with RP had increased perifoveal thickness. CONCLUSIONS: A subset of individuals with CRB1 variants present with mild, adult-onset RP. EOSRD/LCA phenotype was significantly associated with null variants, and 167_169 deletion was exclusively present in the MD cohort. The poor OCT lamination may have a degenerative component, as well as being congenital. Disease symmetry and reasonable window for intervention highlight CRB1 retinal dystrophies as a promising target for trials of novel therapeutics

CERKL-associated retinal dystrophy: Genetics, Phenotype and Natural History

PURPOSE: To analyze the clinical characteristics, natural history, and genetics of CERKL-associated retinal dystrophy in the largest series to date. DESIGN: Multicenter retrospective cohort study. SUBJECTS: 47 patients (37 families) with likely disease-causing CERKL variants METHODS: Review of clinical notes, ophthalmic images, and molecular diagnosis from two international centres. MAIN OUTCOME MEASURES: Visual function, retinal imaging and characteristics were evaluated and correlated. RESULTS: The mean age at the first visit was 29.6 + 13.9 years and the mean follow-up time was 9.1 + 7.4 years. The most frequent initial symptom was central vision loss (40%) and the most common retinal feature was well-demarcated areas of macular atrophy (57%). Seventy percent of the participants had double-null genotypes and 64% had electrophysiological assessment. Amongst the latter, 53% showed similar severity of rod and cone dysfunction, 27% revealed a rod-cone, 10% a cone-rod, and 10% a macular dystrophy dysfunction pattern. Patients without double-null genotypes tended to have fewer pigment deposits and included a higher proportion of older patients with a relatively mild electrophysiological phenotype. Longitudinal analysis showed that over half of the cohort lost 15 ETDRS letters or more in at least one eye during the first 5 years of follow up. CONCLUSIONS: The phenotype of CERKL-retinal dystrophy is broad, encompassing isolated macular disease to severe retina-wide involvement, with a range of functional phenotypes, generally not fitting in the rod-cone/cone-rod dichotomy. Disease onset is often earlier, with more severe retinal degenerative changes and photoreceptor dysfunction, in nullizygous cases

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease

PURPOSE: Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). DESIGN: Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. PARTICIPANTS: Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. METHODS: A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. MAIN OUTCOME MEASURES: We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). RESULTS: An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-k = 0.51[0.49-0.53], and RB-k = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-k = 0.48[0.46-0.50]). CONCLUSIONS: Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references

Clinical and genetic characterization of RDH12-retinal dystrophy in a South American cohort

Purpose: To characterize the largest cohort of individuals with RDH12-retinal dystrophy to date, and the first one from South America. // Design: Retrospective multicenter international study. // Subjects: 78 patients (66 families) with an inherited retinal dystrophy and biallelic variants in RDH12. // Methods: Review of clinical notes, ophthalmic images, and molecular diagnosis. // Main outcome measures: Visual function, retinal imaging and characteristics were evaluated and correlated. // Results: Thirty-seven individuals self-identified as Latino (51%) and 34 as White (47%). Mean age at the baseline visit was 19.8 ± 13 years old (6 months – 46 years old, median 18.5); 41 (53%) were children. Thirty-nine patients (50%) had subsequent visits, with mean follow-up of 6.8 + 7.3 years (0 – 29). Sixty-nine individuals (88%) had Leber congenital amaurosis/early onset severe retinal dystrophy (LCA/EOSRD). Macular and mid-peripheral atrophy was seen in all patients from 3 years of age. A novel retinal finding was a hyperautofluorescent ring in 2 young children with LCA. Eight variants (21%) were previously unreported and the most frequent variant was c.295C>A, p.Leu99Ile, present in 52 alleles of 32 probands. Individuals with LCA homozygous for p.Leu99Ile (31%) had a later age of onset, slower rate of BCVA decrease, the largest percentage of patients with mild visual impairment, and were predicted to reach legal blindness at an older age than the rest of the cohort. // Conclusions: By describing the largest molecularly confirmed cohort to date, improved understanding of disease progression was possible. Our detailed characterization aims to support research and the development of novel therapies that may have the potential to reduce or prevent vision loss in individuals with RDH12-associated retinal dystrophy

Multi-disciplinary team directed analysis of whole genome sequencing reveals pathogenic non-coding variants in molecularly undiagnosed inherited retinal dystrophies

PURPOSE: To identify, using genome sequencing (GS), likely pathogenic non-coding variants in inherited retinal dystrophy (IRD) genes Methods: Patients with IRD were recruited to the study and underwent comprehensive ophthalmological evaluation and GS. The results of GS were investigated through virtual gene panel analysis and plausible pathogenic variants and clinical phenotype evaluated by multi-disciplinary team (MDT) discussion. For unsolved patients in whom a specific gene was suspected to harbour a missed pathogenic variant, targeted re-analysis of non-coding regions was performed on GS data. Candidate variants were functionally tested including by mRNA analysis, minigene and luciferase reporter assays. RESULTS: Previously unreported, likely pathogenic, non-coding variants, in 7 genes (PRPF31, NDP, IFT140, CRB1, USH2A, BBS10, and GUCY2D), were identified in 11 patients. These were shown to lead to mis-splicing (PRPF31, IFT140, CRB1, USH2A) or altered transcription levels (BBS10, GUCY2D). CONCLUSION: MDT-led, phenotype driven, non-coding variant re-analysis of GS is effective in identifying missing causative alleles

Can artificial intelligence accelerate the diagnosis of inherited retinal diseases? Protocol for a data-only retrospective cohort study (Eye2Gene)

Introduction Inherited retinal diseases (IRD) are a leading cause of visual impairment and blindness in the working age population. Mutations in over 300 genes have been found to be associated with IRDs and identifying the affected gene in patients by molecular genetic testing is the first step towards effective care and patient management. However, genetic diagnosis is currently slow, expensive and not widely accessible. The aim of the current project is to address the evidence gap in IRD diagnosis with an AI algorithm, Eye2Gene, to accelerate and democratise the IRD diagnosis service. Methods and analysis The data-only retrospective cohort study involves a target sample size of 10 000 participants, which has been derived based on the number of participants with IRD at three leading UK eye hospitals: Moorfields Eye Hospital (MEH), Oxford University Hospital (OUH) and Liverpool University Hospital (LUH), as well as a Japanese hospital, the Tokyo Medical Centre (TMC). Eye2Gene aims to predict causative genes from retinal images of patients with a diagnosis of IRD. For this purpose, 36 most common causative IRD genes have been selected to develop a training dataset for the software to have enough examples for training and validation for detection of each gene. The Eye2Gene algorithm is composed of multiple deep convolutional neural networks, which will be trained on MEH IRD datasets, and externally validated on OUH, LUH and TMC. Ethics and dissemination This research was approved by the IRB and the UK Health Research Authority (Research Ethics Committee reference 22/WA/0049) ‘Eye2Gene: accelerating the diagnosis of IRDs’ Integrated Research Application System (IRAS) project ID: 242050. All research adhered to the tenets of the Declaration of Helsinki. Findings will be reported in an open-access format