Evidence map›Paper›PMID 36178783›Full record

ArticleTranslational vision science & technology2022

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

Sophie L Glinton, Antonio Calcagni, Watjana Lilaonitkul, Nikolas Pontikos, Sandra Vermeirsch, Gongyu Zhang, Gavin Arno, Siegfried K Wagner, Michel Michaelides, Pearse A Keane and 3 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
1.8field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 3 institutions in 1 country.

Sophie L GlintonInstitute of Ophthalmology, University College London, London, UK.
Antonio CalcagniInstitute of Ophthalmology, University College London, London, UK.
Watjana LilaonitkulInstitute of Health Informatics, University College London, London, UK.
Nikolas PontikosInstitute of Ophthalmology, University College London, London, UK.
Sandra VermeirschMoorfields Eye Hospital NHS Foundation Trust, London, London, UK.
Gongyu ZhangInstitute of Ophthalmology, University College London, London, UK.
Gavin ArnoInstitute of Ophthalmology, University College London, London, UK.
Siegfried K WagnerInstitute of Ophthalmology, University College London, London, UK.
Michel MichaelidesInstitute of Ophthalmology, University College London, London, UK.
Pearse A KeaneInstitute of Ophthalmology, University College London, London, UK.
Andrew R WebsterInstitute of Ophthalmology, University College London, London, UK.
Omar A MahrooInstitute of Ophthalmology, University College London, London, UK.
Anthony G RobsonInstitute of Ophthalmology, University College London, London, UK.
Moorfields Eye Hospital NHS Foundation Trust · GBUniversity College London · GBHealth Data Research UK · GB

Funding

Department of HealthMedical Research Council MR/S004041/1Medical Research Council MR/T019050/1Wellcome TrustWellcome Trust 206619/Z/17/Z
6 · The paper itself

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.

Indexed as

ElectroretinographyRetinal DiseasesATP-Binding Cassette TransportersHumansMachine LearningTomography, Optical CoherenceABCA4 protein, humanATP-Binding Cassette Transporters

Identifiers

PMID36178783
PMCPMC9527330
OpenAlexW4298088505

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.