ArticleScientific reports2019
Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review.BMJ open · 2022Pooled it
- CLEAR report 1: a scoping review and meta-analysis for definitions, imaging metrics, and functional correlates of photoreceptor integrity in AMD.Frontiers in medicine · 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
- Evaluating the Progression of Retinal Sensitivity Loss in Geographic Atrophy Using Machine-Learning-Based Structure-Function Correlation (OMEGA 2).Investigative ophthalmology & visual science · 2025Article
- Targeted Microperimetry Grids for Focal Lesions in Intermediate AMD: PINNACLE Study Report 7.Investigative ophthalmology & visual science · 2025Article
- [Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration].Die Ophthalmologie · 2024Review
- Impact of lens autofluorescence and opacification on retinal imaging.BMJ open ophthalmology · 2024Article
- Spatially Resolved Association of Structural Biomarkers on Retinal Function in Non-Exudative Age-Related Macular Degeneration Over 4 Years.Investigative ophthalmology & visual science · 2024Article
- Associations of presenting visual acuity with morphological changes on OCT in neovascular age-related macular degeneration: PRECISE Study Report 2.Eye (London, England) · 2024Article
- Multimodal imaging and deep learning in geographic atrophy secondary to age-related macular degeneration.Acta ophthalmologica · 2023Review
- Blue-light fundus autofluorescence imaging of pigment epithelial detachments.Eye (London, England) · 2023Article
- Reliability of Retinal Layer Annotation with a Novel, High-Resolution Optical Coherence Tomography Device: A Comparative Study.Bioengineering (Basel, Switzerland) · 2023Article
- Assessment of blood perfusion quality in laparoscopic colorectal surgery by means of Machine Learning.Scientific reports · 2022Article
- From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration.Ophthalmology · 2022Review
- Visual Field Sensitivity Prediction Using Optical Coherence Tomography Analysis in Hydroxychloroquine Toxicity.Investigative ophthalmology & visual science · 2022Article
- Estimation of current and post-treatment retinal function in chronic central serous chorioretinopathy using artificial intelligence.Scientific reports · 2021Article
- AI-based structure-function correlation in age-related macular degeneration.Eye (London, England) · 2021Review
- Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration.Translational vision science & technology · 2021Article
- Fundus-controlled perimetry (microperimetry): Application as outcome measure in clinical trials.Progress in retinal and eye research · 2021Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially-resolved mapping of rod- and cone-function may facilitate monitoring of macular diseases and serve as a functional outcome parameter. However, mesopic and dark-adapted two-color fundus-controlled perimetry (FCP, also called "microperimetry") constitute laborious examinations. We have devised a machine-learning-based approach to predict mesopic and dark-adapted (DA) retinal sensitivity in eyes with neovascular age-related macular degeneration (nAMD). Extensive psychophysical testing and volumetric multimodal retinal imaging data were acquired including mesopic, DA red and DA cyan FCP, spectral-domain optical coherence tomography and confocal scanning laser ophthalmoscopy infrared reflectance and fundus autofluorescence imaging. With patient-wise leave-one-out cross-validation, we have been able to achieve prediction accuracies of (mean absolute error, MAE [95% CI]) 3.94 dB [3.38, 4.5] for mesopic, 4.93 dB [4.59, 5.27] for DA cyan and 4.02 dB [3.63, 4.42] for DA red testing. Partial addition of patient-specific sensitivity data decreased the cross-validated MAE to 2.8 dB [2.51, 3.09], 3.71 dB [3.46, 3.96], and 2.85 dB [2.62, 3.08]. The most important predictive feature was outer nuclear layer thickness. This artificial intelligence-based analysis strategy, termed "inferred sensitivity", herein, enables to estimate differential effects of retinal structural abnormalities on cone- and rod-function in nAMD, and may be used as quasi-functional surrogate endpoint in future clinical trials.
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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.