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

Glinton, Sophie L; Calcagni, Antonio; Lilaonitkul, Watjana; Pontikos, Nikolas; Vermeirsch, Sandra; Zhang, Gongyu; Arno, Gavin; ... Robson, Anthony G; + view all (2022) Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography. Translational Vision Science & Technology (TVST) , 11 (9) , Article 34. 10.1167/tvst.11.9.34. Green open access

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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 effect on retinal function. RESULTS: Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. CONCLUSIONS: This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance: Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.

Type: Article
Title: Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1167/tvst.11.9.34
Publisher version: https://doi.org/10.1167/tvst.11.9.34
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License.
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10156803
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