ArticleScientific reports2021
Inferred retinal sensitivity in recessive Stargardt disease using machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Evaluating the Progression of Retinal Sensitivity Loss in Geographic Atrophy Using Machine-Learning-Based Structure-Function Correlation (OMEGA 2).Investigative ophthalmology & visual science · 2025Article
- Flavoprotein Fluorescence Imaging in Stargardt Disease: Linking Metabolic Stress to Structural Damage.Investigative ophthalmology & visual science · 2025Article
- Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping.International ophthalmology · 2025Review
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- Vertical and horizontal geographic atrophy - A concept to overcome the current structure-function paradox.Eye (London, England) · 2024Article
- Artificial intelligence and machine learning in ophthalmology: A review.Indian journal of ophthalmology · 2023Review
- Photoreceptor degeneration in ABCA4-associated retinopathy and its genetic correlates.JCI insight · 2022Observational
- Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or 'microperimetry'). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function ('inferred sensitivity') based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease. Using nested cross-validation, prediction accuracies of (mean absolute error, MAE [95% CI]) 4.74 dB [4.48-4.99] were achieved. After additional inclusion of limited FCP data, the latter reached 3.89 dB [3.67-4.10] comparable to the test-retest MAE estimate of 3.51 dB [3.11-3.91]. Analysis of the permutation importance revealed, that the IS&OS and RPE thickness were the most important features for the prediction of retinal sensitivity. 'Inferred sensitivity', herein, enables to accurately estimate differential effects of retinal microstructure on spatially-resolved function in Stargardt disease, and might be used as quasi-functional surrogate marker for a refined and time-efficient investigation of possible functionally relevant treatment effects or disease progression.
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Registered trials
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.