Tackling the Challenges of Product Development Through a Collaborative Rare Disease Network: The Foundation Fighting Blindness Consortium

Characterizing the genotypic spectrum of retinitis pigmentosa in East Asian populations: a systematic review

BACKGROUND

Ongoing trials for retinitis pigmentosa (RP) are genotype-specific, with most trials conducted on European cohorts. Due to genetic differences across diverse ancestries and populations, these therapies may not be efficacious in East Asians.

MATERIALS AND METHODS

A literature search was conducted from 1966 to September 2022 for cohort studies on East Asian populations reporting on non-syndromic RP genotypes and variants. Population-weighted prevalence was used to determine the genotypes and individual variants across the entire cohort. The carrier prevalence of common variants was compared against those in Europe.

RESULTS

A total of 12 articles describing 2,932 clinically diagnosed East Asian RP probands were included. We identified 876 variants across 54 genes. The most common genotypes included USH2A, EYS, RPGR, ABCA4, PRPF31, RHO, RP1, RP2, PDE6B and SNRNP200, with USH2A as the most common (17.1%). Overall, 60.5% of probands with clinically relevant variants were found to have one of the genotypes above, with 543/876 (62.0%) of the variants occurring in these genes. The most frequently reported variant was USH2A missense variant c.2802T>G/p.C934W (4.9%). Carrier prevalence of these variants was significantly different (p < 0.0001) than in Europe.

CONCLUSIONS

USH2A was the most commonly affected RP gene in this East Asian cohort, although sub-population analysis revealed distinct genotype prevalence patterns. While the genotypes are similar between East Asia and European cohorts, variants are specific to East Asia. The identification of several prevalent variants in USH2A and EYS provides an opportunity for the development of therapeutics that are relevant for East Asia patients.

Automated Segmentation of Autofluorescence Lesions in Stargardt Disease

OBJECTIVE

To train a deep learning (DL) algorithm to perform fully automated semantic segmentation of multiple autofluorescence lesion types in Stargardt disease.

DESIGN

Cross-sectional study with retrospective imaging data.

SUBJECTS

The study included 193 images from 193 eyes of 97 patients with Stargardt disease.

METHODS

Fundus autofluorescence images obtained from patient visits between 2013 and 2020 were annotated with ground-truth labels. Model training and evaluation were performed using fivefold cross-validation.

MAIN OUTCOMES MEASURES

Dice similarity coefficients, intraclass correlation coefficients, and Bland-Altman analyses comparing algorithm-predicted and grader-labeled segmentations.

RESULTS

The overall Dice similarity coefficient across all lesion classes was 0.78 (95% confidence interval [CI], 0.69-0.86). Dice coefficients were 0.90 (95% CI, 0.85-0.94) for areas of definitely decreased autofluorescence (DDAF), 0.55 (95% CI, 0.35-0.76) for areas of questionably decreased autofluorescence (QDAF), and 0.88 (95% CI, 0.73-1.00) for areas of abnormal background autofluorescence (ABAF). Intraclass correlation coefficients comparing the ground-truth and automated methods were 0.997 (95% CI, 0.996-0.998) for DDAF, 0.863 (95% CI, 0.823-0.895) for QDAF, and 0.974 (95% CI, 0.966-0.980) for ABAF.

CONCLUSIONS

A DL algorithm performed accurate segmentation of autofluorescence lesions in Stargardt disease, demonstrating the feasibility of fully automated segmentation as an alternative to manual or semiautomated labeling methods.