ArticleScientific reports2022
Prediction of visual function from automatically quantified optical coherence tomography biomarkers in patients with geographic atrophy using machine learning.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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.
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Who cites it
18 citing papers in PubMed, 26 citations in OpenAlex.
- Structure-Function Correlations Using Artificial Intelligence-Extracted Retinal Layer Attenuation Measurements in OCT in Eyes with Geographic Atrophy.Ophthalmology science · 2026Article
- Artificial intelligence in retinal care: transforming the doctor-patient partnership.International journal of retina and vitreous · 2026Review
- 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
- Applications of Optical Coherence Tomography in Optic Nerve Head Diseases: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Imaging biomarkers and artificial intelligence for diagnosis, prediction, and therapy of macular fibrosis in age-related macular degeneration: Narrative review and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Early Detection of Macular Atrophy Automated Through 2D and 3D Unet Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Review of Guideline Recommendations for Optimal Anti-VEGF Therapy in Age-Related Macular Degeneration.Life (Basel, Switzerland) · 2024Review
- Association of Lesion Location and Functional Parameters with Vision-Related Quality of Life in Geographic Atrophy Secondary to Age-related Macular Degeneration.Ophthalmology. Retina · 2024Article
- BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.PLOS digital health · 2024Article
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Defining the structure-function relationship of specific lesions in early and advanced age-related macular degeneration.Scientific reports · 2024Article
- Artificial intelligence in age-related macular degeneration: state of the art and recent updates.BMC ophthalmology · 2024Review
- BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.medRxiv : the preprint server for health sciences · 2024Article
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
- An AI model to estimate visual acuity based solely on cross-sectional OCT imaging of various diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Complement inhibitors for age-related macular degeneration.The Cochrane database of systematic reviews · 2023Review
- Topical losartan for established corneal fibrosis with machine learning-based predictors.Therapeutic advances in ophthalmologyArticle
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 3 countries.
Funding
Abstract
Geographic atrophy (GA) is a vision-threatening manifestation of age-related macular degeneration (AMD), one of the leading causes of blindness globally. Objective, rapid, reliable, and scalable quantification of GA from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and clinical endpoints for therapy development. Such automatically quantified biomarkers on OCT are likely to further elucidate structure-function correlation in GA and thus the pathophysiological mechanisms of disease development and progression. In this work, we aimed to predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in GA. A post-hoc analysis of data from a clinical trial and routine clinical care was conducted. A deep-learning automated segmentation model was applied on OCT scans from 476 eyes (325 patients) with GA. A separate machine learning prediction model (Random Forest) used the resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under standard (VA) and low luminance (LLVA). The primary outcome was regression coefficient (r
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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.