ReviewEye (London, England)2021
AI-based structure-function correlation in age-related macular degeneration.
Review in Eye (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Retinal sensitivity changes in early/intermediate AMD: a systematic review and meta-analysis of visual field testing under mesopic and scotopic lighting.Eye (London, England) · 2024Pooled it
- Visual Function Assessment in Geographic Atrophy: A Review.Clinical & experimental ophthalmology · 2026Review
- Predicting visual acuity using optical coherence tomography in patients with neovascular age-related macular degeneration.Scientific reports · 2025Article
- Lesion detection in age-related macular degeneration with a multi-modal imaging and machine learning approach.Biomedical engineering online · 2025Article
- Geographic Atrophy in Age-Related Macular Degeneration.Deutsches Arzteblatt international · 2025Review
- Macular function measured by Nidek MP-3 microperimeter in normal Chinese individuals across age groups.PeerJ · 2025Article
- Inherited Retinal Degenerations and Non-Neovascular Age-Related Macular Degeneration: Progress and Unmet Needs.Translational vision science & technology · 2024Review
- Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration.Scientific reports · 2024Article
- [Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration].Die Ophthalmologie · 2024Review
- Defining the structure-function relationship of specific lesions in early and advanced age-related macular degeneration.Scientific reports · 2024Article
- A machine learning framework for the quantification of experimental uveitis in murine OCT.Biomedical optics express · 2023Article
- Age-Related Macular Degeneration: Epidemiology, Pathophysiology, Diagnosis, and Treatment.Cureus · 2022Review
- Automated foveal location detection on spectral-domain optical coherence tomography in geographic atrophy patients.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- Artificial Intelligence Analysis of Biofluid Markers in Age-Related Macular Degeneration: A Systematic Review.Clinical ophthalmology (Auckland, N.Z.) · 2022Review
- Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism.Journal of clinical medicine · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-related macular degeneration (AMD). A recent development is the implementation of artificial intelligence (AI) to infer results of psychophysical examinations based on findings derived from multimodal imaging. We conducted a review of the current literature referenced in PubMed and Web of Science among others with the keywords 'artificial intelligence' and 'machine learning' in combination with 'perimetry', 'best-corrected visual acuity (BCVA)', 'retinal function' and 'age-related macular degeneration'. So far AI-based structure-function correlations have been applied to infer conventional visual field, fundus-controlled perimetry, and electroretinography data, as well as BCVA, and patient-reported outcome measures (PROM). In neovascular AMD, inference of BCVA (hereafter termed inferred BCVA) can estimate BCVA results with a root mean squared error of ~7-11 letters, which is comparable to the accuracy of actual visual acuity assessment. Further, AI-based structure-function correlation can successfully infer fundus-controlled perimetry (FCP) results both for mesopic as well as dark-adapted (DA) cyan and red testing (hereafter termed inferred sensitivity). Accuracy of inferred sensitivity can be augmented by adding short FCP examinations and reach mean absolute errors (MAE) of ~3-5 dB for mesopic, DA cyan and DA red testing. Inferred BCVA, and inferred retinal sensitivity, based on multimodal imaging, may be considered as a quasi-functional surrogate endpoint for future interventional clinical trials in the future.
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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.