Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography.

ABSTRACT:

Purpose

Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype-phenotype correlations.

Methods

International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effe

SUBMITTER: Glinton SL 

PROVIDER: S-EPMC9527330 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature