Deciphering novel TCF4-driven mechanisms underlying a common triplet repeat expansion-mediated disease.

Fuchs endothelial corneal dystrophy (FECD) is an age-related cause of vision loss, and the most common repeat expansion-mediated disease in humans characterised to date. Up to 80% of European FECD cases have been attributed to expansion of a non-coding CTG repeat element (termed CTG18.1) located within the ubiquitously expressed transcription factor encoding gene, TCF4. The non-coding nature of the repeat and the transcriptomic complexity of TCF4 have made it extremely challenging to experimentally decipher the molecular mechanisms underlying this disease. Here we comprehensively describe CTG18.1 expansion-driven molecular components of disease within primary patient-derived corneal endothelial cells (CECs), generated from a large cohort of individuals with CTG18.1-expanded (Exp+) and CTG 18.1-independent (Exp-) FECD. We employ long-read, short-read, and spatial transcriptomic techniques to interrogate expansion-specific transcriptomic biomarkers. Interrogation of long-read sequencing and alternative splicing analysis of short-read transcriptomic data together reveals the global extent of altered splicing occurring within Exp+ FECD, and unique transcripts associated with CTG18.1-expansions. Similarly, differential gene expression analysis highlights the total transcriptomic consequences of Exp+ FECD within CECs. Furthermore, differential exon usage, pathway enrichment and spatial transcriptomics reveal TCF4 isoform ratio skewing solely in Exp+ FECD with potential downstream functional consequences. Lastly, exome data from 134 Exp- FECD cases identified rare (minor allele frequency <0.005) and potentially deleterious (CADD>15) TCF4 variants in 7/134 FECD Exp- cases, suggesting that TCF4 variants independent of CTG18.1 may increase FECD risk. In summary, our study supports the hypothesis that at least two distinct pathogenic mechanisms, RNA toxicity and TCF4 isoform-specific dysregulation, both underpin the pathophysiology of FECD. We anticipate these data will inform and guide the development of translational interventions for this common triplet-repeat mediated disease.

Pegcetacoplan Treatment and Consensus Features of Geographic Atrophy Over 24 Months.

Importance: Despite widespread availability and consensus on its advantages for detailed imaging of geographic atrophy (GA), spectral-domain optical coherence tomography (SD-OCT) might benefit from automated quantitative OCT analyses in GA diagnosis, monitoring, and reporting of its landmark clinical trials. Objective: To analyze the association between pegcetacoplan and consensus GA SD-OCT end points. Design, Setting, and Participants: This was a post hoc analysis of 11 614 SD-OCT volumes from 936 of the 1258 participants in 2 parallel phase 3 studies, the Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (OAKS) and Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (DERBY). OAKS and DERBY were 24-month, multicenter, randomized, double-masked, sham-controlled studies conducted from August 2018 to July 2020 among adults with GA with total area 2.5 to 17.5 mm2 on fundus autofluorescence imaging (if multifocal, at least 1 lesion ≥1.25 mm2). This analysis was conducted from September to December 2023. Interventions: Study participants received pegcetacoplan, 15 mg per 0.1-mL intravitreal injection, monthly or every other month, or sham injection monthly or every other month. Main Outcomes and Measures: The primary end point was the least squares mean change from baseline in area of retinal pigment epithelium and outer retinal atrophy in each of the 3 treatment arms (pegcetacoplan monthly, pegcetacoplan every other month, and pooled sham [sham monthly and sham every other month]) at 24 months. Feature-specific area analysis was conducted by Early Treatment Diabetic Retinopathy Study (ETDRS) regions of interest (ie, foveal, parafoveal, and perifoveal). Results: Among 936 participants, the mean (SD) age was 78.5 (7.22) years, and 570 participants (60.9%) were female. Pegcetacoplan, but not sham treatment, was associated with reduced growth rates of SD-OCT biomarkers for GA for up to 24 months. Reductions vs sham in least squares mean (SE) change from baseline of retinal pigment epithelium and outer retinal atrophy area were detectable at every time point from 3 through 24 months (least squares mean difference vs pooled sham at month 24, pegcetacoplan monthly: -0.86 mm2; 95% CI, -1.15 to -0.57; P < .001; pegcetacoplan every other month: -0.69 mm2; 95% CI, -0.98 to -0.39; P < .001). This association was more pronounced with more frequent dosing (pegcetacoplan monthly vs pegcetacoplan every other month at month 24: -0.17 mm2; 95% CI, -0.43 to 0.08; P = .17). Stronger associations were observed in the parafoveal and perifoveal regions for both pegcetacoplan monthly and pegcetacoplan every other month. Conclusions and Relevance: These findings offer additional insight into the potential effects of pegcetacoplan on the development of GA, including potential effects on the retinal pigment epithelium and photoreceptors. Trial Registration: ClinicalTrials.gov Identifiers: NCT03525600 and NCT03525613.

Sex Distributions in Non-ABCA4 Autosomal Macular Dystrophies.

Purpose: We sought to explore whether sex imbalances are discernible in several autosomally inherited macular dystrophies. Methods: We searched the electronic patient records of our large inherited retinal disease cohort, quantifying numbers of males and females with the more common (non-ABCA4) inherited macular dystrophies (associated with BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3). BEST1 cases were subdivided into typical autosomal dominant and recessive disease. For PRPH2, only patients with variants at codons 172 or 142 were included. Recessive PROM1 and recessive RP1L1 cases were excluded because these variants give a more widespread or peripheral degeneration. The proportion of females was calculated for each condition; two-tailed binomial testing was performed. Where a significant imbalance was found, previously published cohorts were also explored. Results: Of 325 patients included, numbers for BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3 were 152, 35, 30, 50, 14, and 44, respectively. For autosomal dominant Best disease (n = 115), there were fewer females (38%; 95% confidence interval [CI], 29-48%; P = 0.015). For EFEMP1-associated disease (n = 35), there were significantly more females (77%; 95% CI, 60%-90%; P = 0.0019). No significant imbalances were seen for the other genes. When pooling our cohort with previous large dominant Best disease cohorts, the proportion of females was 37% (95% CI, 31%-43%; P = 1.2 × 10-5). Pooling previously published EFEMP1-cases with ours yielded an overall female proportion of 62% (95% CI, 54%-69%; P = 0.0023). Conclusions: This exploratory study found significant sex imbalances in two autosomal macular dystrophies, suggesting that sex could be a modifier. Our findings invite replication in further cohorts and the investigation of potential mechanisms.

Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3000 Inherited Retinal Disease Patients from the United Kingdom.

Purpose: To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Design: Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Participants: Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Methods: Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire imaging dataset. Main Outcome Measures: Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. Results: A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. Conclusions: We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.

A comparison of keratoconus progression following collagen cross-linkage using standard or personalised keratometry thresholds.

OBJECTIVE: To define how estimates of keratoconus progression following collagen cross-linking (CXL) vary according to the parameter selected to measure corneal shape. MATERIALS AND METHODS: We estimated progression following CXL in 1677 eyes. We compared standard definitions of keratoconus progression based on published thresholds for Kmax, front K2, or back K2, or progression of any two of these three parameters, with the option of an increased threshold for Kmax values ≥ 55D. As corneal thickness reduces unpredictably after CXL, it was excluded from the principal analysis. We then repeated the analysis using novel adaptive estimates of progression for Kmax, front K2, or back K2, developed separately using 6463 paired readings from keratoconus eyes, with a variation of the Bland-Altman method to determine the 95% regression-based limits of agreement (LoA). We created Kaplan-Meier survival plots for both standard and adaptive thresholds. The primary outcome was progression five years after a baseline visit 9-15 months following CXL. RESULTS: Progression rates were 8% with a standard (≥ 1.5D) threshold for K2 or 6% with the static multi-parameter definition. With a ≥ 1D threshold for Kmax, the progression was significantly higher at 29%. With adaptive Kmax or K2, the progression rates were similar (20%) but less than with the adaptive multi-parameter method (22%). CONCLUSIONS: Estimates of keratoconus progression following CXL vary widely according to the reference criteria. Using adaptive thresholds (LoA) to define the repeatability of keratometry gives estimates for progression that are markedly higher than with the standard multi-parameter method.

Phenotyping and genotyping inherited retinal diseases: Molecular genetics, clinical and imaging features, and therapeutics of macular dystrophies, cone and cone-rod dystrophies, rod-cone dystrophies, Leber congenital amaurosis, and cone dysfunction syndromes.

Inherited retinal diseases (IRD) are a leading cause of blindness in the working age population and in children. The scope of this review is to familiarise clinicians and scientists with the current landscape of molecular genetics, clinical phenotype, retinal imaging and therapeutic prospects/completed trials in IRD. Herein we present in a comprehensive and concise manner: (i) macular dystrophies (Stargardt disease (ABCA4), X-linked retinoschisis (RS1), Best disease (BEST1), PRPH2-associated pattern dystrophy, Sorsby fundus dystrophy (TIMP3), and autosomal dominant drusen (EFEMP1)), (ii) cone and cone-rod dystrophies (GUCA1A, PRPH2, ABCA4, KCNV2 and RPGR), (iii) predominant rod or rod-cone dystrophies (retinitis pigmentosa, enhanced S-Cone syndrome (NR2E3), Bietti crystalline corneoretinal dystrophy (CYP4V2)), (iv) Leber congenital amaurosis/early-onset severe retinal dystrophy (GUCY2D, CEP290, CRB1, RDH12, RPE65, TULP1, AIPL1 and NMNAT1), (v) cone dysfunction syndromes (achromatopsia (CNGA3, CNGB3, PDE6C, PDE6H, GNAT2, ATF6), X-linked cone dysfunction with myopia and dichromacy (Bornholm Eye disease; OPN1LW/OPN1MW array), oligocone trichromacy, and blue-cone monochromatism (OPN1LW/OPN1MW array)). Whilst we use the aforementioned classical phenotypic groupings, a key feature of IRD is that it is characterised by tremendous heterogeneity and variable expressivity, with several of the above genes associated with a range of phenotypes.

Distinct Clinical Effects of Two RP1L1 Hotspots in East Asian Patients With Occult Macular Dystrophy (Miyake Disease): EAOMD Report 4.

Purpose: To characterize the clinical effects of two RP1L1 hotspots in patients with East Asian occult macular dystrophy (OMD). Methods: Fifty-one patients diagnosed with OMD harboring monoallelic pathogenic RP1L1 variants (Miyake disease) from Japan, South Korea, and China were enrolled. Patients were classified into two genotype groups: group A, p.R45W, and group B, missense variants located between amino acids (aa) 1196 and 1201. The clinical parameters of the two genotypes were compared, and deep learning based on spectral-domain optical coherence tomographic (SD-OCT) images was used to distinguish the morphologic differences. Results: Groups A and B included 29 and 22 patients, respectively. The median age of onset in groups A and B was 14.0 and 40.0 years, respectively. The median logMAR visual acuity of groups A and B was 0.70 and 0.51, respectively, and the survival curve analysis revealed a 15-year difference in vision loss (logMAR 0.22). A statistically significant difference was observed in the visual field classification, but no significant difference was found in the multifocal electroretinographic classification. High accuracy (75.4%) was achieved in classifying genotype groups based on SD-OCT images using machine learning. Conclusions: Distinct clinical severities and morphologic phenotypes supported by artificial intelligence-based classification were derived from the two investigated RP1L1 hotspots: a more severe phenotype (p.R45W) and a milder phenotype (1196-1201 aa). This newly identified genotype-phenotype association will be valuable for medical care and the design of therapeutic trials.

Spectrum of Genetic Variants in the Most Common Genes Causing Inherited Retinal Disease in a Large Molecularly Characterized United Kingdom Cohort.

PURPOSE: Inherited retinal disease (IRD) is a leading cause of blindness. Recent advances in gene-directed therapies highlight the importance of understanding the genetic basis of these disorders. This study details the molecular spectrum in a large United Kingdom (UK) IRD patient cohort. DESIGN: Retrospective study of electronic patient records. PARTICIPANTS: Patients with IRD who attended the Genetics Service at Moorfields Eye Hospital between 2003 and July 2020, in whom a molecular diagnosis was identified. METHODS: Genetic testing was undertaken via a combination of single-gene testing, gene panel testing, whole exome sequencing, and more recently, whole genome sequencing. Likely disease-causing variants were identified from entries within the genetics module of the hospital electronic patient record (OpenEyes Electronic Medical Record). Analysis was restricted to only genes listed in the Genomics England PanelApp R32 Retinal Disorders panel (version 3.24), which includes 412 genes associated with IRD. Manual curation ensured consistent variant annotation and included only plausible disease-associated variants. MAIN OUTCOME MEASURES: Detailed analysis was performed for variants in the 5 most frequent genes (ABCA4, USH2A, RPGR, PRPH2, and BEST1), as well as for the most common variants encountered in the IRD study cohort. RESULTS: We identified 4415 individuals from 3953 families with molecularly diagnosed IRD (variants in 166 genes). Of the families, 42.7% had variants in 1 of the 5 most common IRD genes. Complex disease alleles contributed to disease in 16.9% of affected families with ABCA4-associated retinopathy. USH2A exon 13 variants were identified in 43% of affected individuals with USH2A-associated IRD. Of the RPGR variants, 71% were clustered in the ORF15 region. PRPH2 and BEST1 variants were associated with a range of dominant and recessive IRD phenotypes. Of the 20 most prevalent variants identified, 5 were not in the most common genes; these included founder variants in CNGB3, BBS1, TIMP3, EFEMP1, and RP1. CONCLUSIONS: We describe the most common pathogenic IRD alleles in a large single-center multiethnic UK cohort and the burden of disease, in terms of families affected, attributable to these variants. Our findings will inform IRD diagnoses in future patients and help delineate the cohort of patients eligible for gene-directed therapies under development. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment.

OBJECTIVE: To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth. METHODS: Post hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area. RESULTS: Eyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively) CONCLUSION: The OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.

RP2-Associated X-linked Retinopathy: Clinical Findings, Molecular Genetics, and Natural History in a Large Cohort of Female Carriers.

PURPOSE: RP2-associated retinopathy typically causes severe early onset retinitis pigmentosa (RP) in affected males. However, there is a scarcity of reports describing the clinical phenotype of female carriers. We tested the hypothesis that RP2 variants manifest in female carriers with a range of functional and anatomic characteristics. DESIGN: Retrospective case series. METHODS: Females with disease-causing variants in RP2 were identified from investigation of pedigrees affected by RP2 retinopathy. All case notes and results of molecular genetic testing, retinal imaging (fundus autofluorescence imaging, optical coherence tomography (OCT)), and electrophysiology were reviewed. RESULTS: Forty pedigrees were investigated. Twenty-nine pedigrees had obligate carriers or molecularly confirmed female members with recorded relevant history and/or examination. For 8 pedigrees, data were available only from history, with patients reporting affected female relatives with RP in 4 cases and unaffected female relatives in the other 4 cases. Twenty-seven females from 21 pedigrees were examined by a retinal genetics specialist. Twenty-three patients (85%) reported no complaints and had normal vision and 4 patients had RP-associated complaints (15%). Eight patients had normal fundus examination (30%), 10 had a tapetal-like reflex (TLR; 37%), 5 had scattered peripheral pigmentation (19%), and the 4 symptomatic patients had fundus findings compatible with RP (15%). All asymptomatic patients with normal fundus, TLR, or asymptomatic pigmentary changes had a continuous ellipsoid zone on OCT when available. The electroretinograms revealed mild to severe photoreceptor dysfunction in 9 of 11 subjects, often asymmetrical, including 5 with pattern electroretinogram evidence of symmetrical (n = 4) or unilateral (n = 1 subject) macular dysfunction. CONCLUSIONS: Most carriers were asymptomatic, exhibiting subclinical characteristics such as TLR and pigmentary changes. However, female carriers of RP2 variants can manifest RP. Family history of affected females with RP does not exclude X-linked disease. The phenotypic spectrum as described herein has prognostic and counselling implications for RP2 carriers and patients.

RBP3-Retinopathy-Inherited High Myopia and Retinal Dystrophy: Genetic Characterization, Natural History, and Deep Phenotyping.

PURPOSE: To examine the genetic and clinical features and the natural history of RBP3-associated retinopathy. DESIGN: Multi-center international, retrospective, case series of adults and children, with moleculraly confirmed RBP3-asociated retinopathy. METHODS: The genetic, clinical, and retinal imaging findings, including optical coherence tomography (OCT) and fundus autofluorescence (FAF), were investigated both cross-sectionally and longitudinally. The results of international standard full-field electroretinography (ERG) and pattern electroretinography (PERG) were reviewed. RESULTS: We ascertained 12 patients (5 female and 7 male) from 10 families (4 patients previously reported). Ten novel disease-causing RBP3 variants were identified. Ten patients were homozygous. The mean age (±SD, range) of the group was 21.4 years (±19.1, 2.9-60.5 years) at baseline evaluation. All 12 patients were highly myopic, with a mean spherical equivalent of -16.0D (range, -7.0D to -33.0D). Visual acuity was not significantly different between eyes, and no significant anisometropia was observed. Mean best-corrected visual acuity (BCVA) was 0.48 logMAR (SD, ±0.29; range, 0.2-1.35 logMAR); at baseline. Eleven patients had longitudinal BCVA assessment, with a mean BCVA of 0.46 logMAR after a mean follow-up of 12.6 years. All patients were symptomatic with reduced VA and myopia by the age of 7 years old. All patients had myopic fundi and features in keeping with high myopia on OCT, including choroidal thinning. The 4 youngest patients had no fundus pigmentary changes, with the rest of the patients presenting with a variable degree of mid-peripheral pigmentation and macular changes. FAF showed variable phenotypes, ranging from areas of increased signal to advanced atrophy in older patients. OCT showed cystoid macular edema at presentation in 3 patients, which persisted during follow-up in 2 patients and resolved to atrophy in the third patient. The ERGs were abnormal in 9 of 9 cases, revealing variable relative involvement of rod and cone photoreceptors with additional milder dysfunction post-phototransduction in some. All but 1 patient had PERG evidence of macular dysfunction, which was severe in most cases. CONCLUSIONS: This study details the clinical and functional phenotype of RBP3-retinopathy in the largest cohort reported to date. RBP3-retinopathy is a disease characterized by early onset, slow progression over decades, and high myopia. The phenotypic spectrum and natural history as described herein has prognostic and counseling implications. RBP3-related disease should be considered in children with high myopia and retinal dystrophy.

KCNV2-associated retinopathy: genotype-phenotype correlations - KCNV2 study group report 3.

BACKGROUND/AIMS: To investigate genotype-phenotype associations in patients with KCNV2 retinopathy. METHODS: Review of clinical notes, best-corrected visual acuity (BCVA), molecular variants, electroretinography (ERG) and retinal imaging. Subjects were grouped according to the combination of KCNV2 variants-two loss-of-function (TLOF), two missense (TM) or one of each (MLOF)-and parameters were compared. RESULTS: Ninety-two patients were included. The mean age of onset (mean±SD) in TLOF (n=55), TM (n=23) and MLOF (n=14) groups was 3.51±0.58, 4.07±2.76 and 5.54±3.38 years, respectively. The mean LogMAR BCVA (±SD) at baseline in TLOF, TM and MLOF groups was 0.89±0.25, 0.67±0.38 and 0.81±0.35 for right, and 0.88±0.26, 0.69±0.33 and 0.78±0.33 for left eyes, respectively. The difference in BCVA between groups at baseline was significant in right (p=0.03) and left eyes (p=0.035). Mean outer nuclear layer thickness (±SD) at baseline in TLOF, MLOF and TM groups was 37.07±15.20 µm, 40.67±12.53 and 40.38±18.67, respectively, which was not significantly different (p=0.85). The mean ellipsoid zone width (EZW) loss (±SD) was 2051 µm (±1318) for patients in the TLOF, and 1314 µm (±965) for MLOF. Only one patient in the TM group had EZW loss at presentation. There was considerable overlap in ERG findings, although the largest DA 10 ERG b-waves were associated with TLOF and the smallest with TM variants. CONCLUSIONS: Patients with missense alterations had better BCVA and greater structural integrity. This is important for patient prognostication and counselling, as well as stratification for future gene therapy trials.

Evaluating the Effects of C3 Inhibition on Geographic Atrophy Progression from Deep-Learning OCT Quantification: A Split-Person Study.

INTRODUCTION: To evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification. METHODS: A post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye. RESULTS: Annual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (- 0.735 vs. - 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21). CONCLUSIONS: Pegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF. TRIAL REGISTRATION: Clinical Trials identifier: NCT02503332.

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.

Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study.

BACKGROUND: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available experts has raised concerns about the sustainability of this approach. We aimed to develop bespoke and code-free deep learning-based classifiers for plus disease, a hallmark of ROP, in an ethnically diverse population in London, UK, and externally validate them in ethnically, geographically, and socioeconomically diverse populations in four countries and three continents. Code-free deep learning is not reliant on the availability of expertly trained data scientists, thus being of particular potential benefit for low resource health-care settings. METHODS: This retrospective cohort study used retinal images from 1370 neonates admitted to a neonatal unit at Homerton University Hospital NHS Foundation Trust, London, UK, between 2008 and 2018. Images were acquired using a Retcam Version 2 device (Natus Medical, Pleasanton, CA, USA) on all babies who were either born at less than 32 weeks gestational age or had a birthweight of less than 1501 g. Each images was graded by two junior ophthalmologists with disagreements adjudicated by a senior paediatric ophthalmologist. Bespoke and code-free deep learning models (CFDL) were developed for the discrimination of healthy, pre-plus disease, and plus disease. Performance was assessed internally on 200 images with the majority vote of three senior paediatric ophthalmologists as the reference standard. External validation was on 338 retinal images from four separate datasets from the USA, Brazil, and Egypt with images derived from Retcam and the 3nethra neo device (Forus Health, Bengaluru, India). FINDINGS: Of the 7414 retinal images in the original dataset, 6141 images were used in the final development dataset. For the discrimination of healthy versus pre-plus or plus disease, the bespoke model had an area under the curve (AUC) of 0·986 (95% CI 0·973-0·996) and the CFDL model had an AUC of 0·989 (0·979-0·997) on the internal test set. Both models generalised well to external validation test sets acquired using the Retcam for discriminating healthy from pre-plus or plus disease (bespoke range was 0·975-1·000 and CFDL range was 0·969-0·995). The CFDL model was inferior to the bespoke model on discriminating pre-plus disease from healthy or plus disease in the USA dataset (CFDL 0·808 [95% CI 0·671-0·909, bespoke 0·942 [0·892-0·982]], p=0·0070). Performance also reduced when tested on the 3nethra neo imaging device (CFDL 0·865 [0·742-0·965] and bespoke 0·891 [0·783-0·977]). INTERPRETATION: Both bespoke and CFDL models conferred similar performance to senior paediatric ophthalmologists for discriminating healthy retinal images from ones with features of pre-plus or plus disease; however, CFDL models might generalise less well when considering minority classes. Care should be taken when testing on data acquired using alternative imaging devices from that used for the development dataset. Our study justifies further validation of plus disease classifiers in ROP screening and supports a potential role for code-free approaches to help prevent blindness in vulnerable neonates. FUNDING: National Institute for Health Research Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and the University College London Institute of Ophthalmology. TRANSLATIONS: For the Portuguese and Arabic translations of the abstract see Supplementary Materials section.

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.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

INTRODUCTION: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. AIMS AND METHODS: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. RESULTS: Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). CONCLUSIONS: Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. IMPLICATIONS: This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

Phenotype and genotype of concurrent keratoconus and Fuchs endothelial corneal dystrophy.

PURPOSE: To characterise the phenotype and genotype of concurrent keratoconus and Fuchs endothelial corneal dystrophy (KC + FECD). METHODS: We recruited 20 patients with concurrent KC + FECD for a retrospective observational case series from the United Kingdom and the Czech Republic. We compared eight parameters of corneal shape (Pentacam, Oculus) with two groups of age-matched controls who had either isolated keratoconus (KC) or isolated FECD. We genotyped probands for an intronic triplet TCF4 repeat expansion (CTG18.1) and the ZEB1 variant c.1920G >T p.(Gln640His). RESULTS: The median age at diagnosis of patients with KC + FECD was 54 (interquartile range 46 to 66) years, with no evidence of KC progression (median follow-up 84 months, range 12 to 120 months). The mean (standard deviation (SD)) of the minimum corneal thickness, 493 (62.7) µm, was greater than eyes with KC, 458 (51.1) µm, but less than eyes with FECD, 590 (55.6) µm. Seven other parameters of corneal shape were more like KC than FECD. Seven (35%) probands with KC + FECD had a TCF4 repeat expansion of ≥50 compared to five controls with isolated FECD. The average of the largest TCF4 expansion in cases with KC + FECD (46 repeats, SD 36 repeats) was similar to the age-matched controls with isolated FECD (36 repeats, SD 28 repeats; p = 0.299). No patient with KC + FECD harboured the ZEB1 variant. CONCLUSIONS: The KC + FECD phenotype is consistent with KC but with superimposed stromal swelling from endothelial disease. The proportion of cases with a TCF4 expansion is similar in concurrent KC + FECD and age-matched controls with isolated FECD.

Association Between Retinal Features From Multimodal Imaging and Schizophrenia.

Importance: The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts. Objective: To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population. Design, Setting, and Participants: This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022. Main Outcomes and Measures: Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers. Results: A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 µm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 µm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 µm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes. Conclusions and Relevance: In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.

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.