ArticleScientific reports2022
A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers.
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 25 papers, 2 of them syntheses 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.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 47 citations in OpenAlex.
- Multimodal artificial intelligence in retinal vascular and neovascular macular diseases: a systematic review of diagnostic and prognostic applications.BMC ophthalmology · 2025Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Clinical validation of artificial intelligence algorithms for the detection of different central-involved retinal pathologies and glaucoma from non-mydriatic images.Frontiers in artificial intelligence · 2026Trial
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- Artificial Intelligence Applications in Sickle Cell Retinopathy Imaging: Current Progress, Challenges, and Future Directions.Journal of ophthalmology · 2026Review
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
- Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model.Ophthalmology. Retina · 2025Article
- Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Review
- Evolutionary patterns and research frontiers of artificial intelligence in age-related macular degeneration: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2025Article
- Machine learning models for risk prediction of age-related macular degeneration in Fujian eye study.PloS one · 2025Article
- Recent advances in the application of artificial intelligence in age-related macular degeneration.BMJ open ophthalmology · 2024Review
- Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence.Scientific reports · 2024Article
- OCTA-based AMD Stage Grading Enhancement via Class-Conditioned Style Transfer.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Automated machine learning model for fundus image classification by health-care professionals with no coding experience.Scientific reports · 2024Article
- Artificial intelligence in age-related macular degeneration: state of the art and recent updates.BMC ophthalmology · 2024Review
- Multimodal and multi-omics-based deep learning model for screening of optic neuropathy.Heliyon · 2023Article
- Biomarkers for the Progression of Intermediate Age-Related Macular Degeneration.Ophthalmology and therapy · 2023Review
- Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review.Ophthalmology and therapy · 2023Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Within the next 1.5 decades, 1 in 7 U.S. adults is anticipated to suffer from age-related macular degeneration (AMD), a degenerative retinal disease which leads to blindness if untreated. Optical coherence tomography angiography (OCTA) has become a prime technique for AMD diagnosis, specifically for late-stage neovascular (NV) AMD. Such technologies generate massive amounts of data, challenging to parse by experts alone, transforming artificial intelligence into a valuable partner. We describe a deep learning (DL) approach which achieves multi-class detection of non-AMD vs. non-neovascular (NNV) AMD vs. NV AMD from a combination of OCTA, OCT structure, 2D b-scan flow images, and high definition (HD) 5-line b-scan cubes; DL also detects ocular biomarkers indicative of AMD risk. Multimodal data were used as input to 2D-3D Convolutional Neural Networks (CNNs). Both for CNNs and experts, choroidal neovascularization and geographic atrophy were found to be important biomarkers for AMD. CNNs predict biomarkers with accuracy up to 90.2% (positive-predictive-value up to 75.8%). Just as experts rely on multimodal data to diagnose AMD, CNNs also performed best when trained on multiple inputs combined. Detection of AMD and its biomarkers from OCTA data via CNNs has tremendous potential to expedite screening of early and late-stage AMD patients.
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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.