ArticleJournal of clinical medicine2023
A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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
7 citing papers in PubMed.
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- A Matching-Adjusted Comparison of Faricimab and Aflibercept 8 mg in Neovascular Age-Related Macular Degeneration and Diabetic Macular Edema.Ophthalmic research · 2026Article
- Blueprint of Collapse: Precision Biomarkers, Molecular Cascades, and the Engineered Decline of Fast-Progressing ALS.International journal of molecular sciences · 2025Review
- Visual outcome following initiation of first injection versus after three monthly doses of aflibercept 2 mg for treatment naïve age-related macular degeneration to inform clinical trial designs: PRECISE Report No. 6.Eye (London, England) · 2025Article
- Dual pathway inhibition with faricimab for previously treated neovascular age-related macular degeneration and diabetic macular oedema: guidance from a UK panel of retina specialists.Eye (London, England) · 2024Article
- Advances and prospects of multi-modal ophthalmic artificial intelligence based on deep learning: a review.Eye and vision (London, England) · 2024Review
- Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.Survey of ophthalmologyReview
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
Patients diagnosed with exudative neovascular age-related macular degeneration are commonly treated with anti-vascular endothelial growth factor (anti-VEGF) agents. However, response to treatment is heterogeneous, without a clinical explanation. Predicting suboptimal response at baseline will enable more efficient clinical trial designs for novel, future interventions and facilitate individualised therapies. In this multicentre study, we trained a multi-modal artificial intelligence (AI) system to identify suboptimal responders to the loading-phase of the anti-VEGF agent aflibercept from baseline characteristics. We collected clinical features and optical coherence tomography scans from 1720 eyes of 1612 patients between 2019 and 2021. We evaluated our AI system as a patient selection method by emulating hypothetical clinical trials of different sizes based on our test set. Our method detected up to 57.6% more suboptimal responders than random selection, and up to 24.2% more than any alternative selection criteria tested. Applying this method to the entry process of candidates into randomised controlled trials may contribute to the success of such trials and further inform personalised care.
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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.