ArticleTranslational vision science & technology2020
Development of Deep Learning Models to Predict Best-Corrected Visual Acuity from Optical Coherence Tomography.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00891735 (A Phase III, Double-masked, Multicenter, Randomized, Active Treatment-controlled Study of the Efficacy and Safety of 0.5 mg and 2.0 mg Ranibizumab Administered Monthly or on an As-needed Basis), which is not on this map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
A Phase III, Double-masked, Multicenter, Randomized, Active Treatment-controlled Study of the Efficacy and Safety of 0.5 mg and 2.0 mg Ranibizumab Administered Monthly or on an As-needed Basis (PRN) in Patients With Subfoveal Neovascular Age-related Macular Degeneration
Who cites it
18 citing papers in PubMed, 39 citations in OpenAlex.
- AI-Based Visual Prognosis in Full-Thickness Macular Hole Surgery Using the ILM Flap Technique.Beyoglu eye journal · 2026Article
- Predicting visual acuity using optical coherence tomography in patients with neovascular age-related macular degeneration.Scientific reports · 2025Article
- Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.Scientific reports · 2024Article
- Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration.Pharmaceuticals (Basel, Switzerland) · 2024Review
- Prediction of Post-Treatment Visual Acuity in Age-Related Macular Degeneration Patients With an Interpretable Machine Learning Method.Translational vision science & technology · 2024Article
- Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review.International journal of retina and vitreous · 2024Review
- Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.Bioengineering (Basel, Switzerland) · 2023Article
- Machine Learning to Predict Response to Ranibizumab in Neovascular Age-Related Macular Degeneration.Ophthalmology science · 2023Article
- Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.The British journal of ophthalmology · 2023Article
- 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
- Article
- The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method.Frontiers in medicine · 2023Article
- Predicting Visual Acuity in Patients Treated for AMD.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning-based artificial intelligence.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- Performance of Deep Learning Models in Automatic Measurement of Ellipsoid Zone Area on Baseline Optical Coherence Tomography (OCT) Images From the Rate of Progression of USH2A-Related Retinal Degeneration (RUSH2A) Study.Frontiers in medicine · 2022Article
- Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021Article
- Machine Learning to Predict Faricimab Treatment Outcome in Neovascular Age-Related Macular Degeneration.Ophthalmology scienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop deep learning (DL) models to predict best-corrected visual acuity (BCVA) from optical coherence tomography (OCT) images from patients with neovascular age-related macular degeneration (nAMD). Methods: Retrospective analysis of OCT images and associated BCVA measurements from the phase 3 HARBOR trial (NCT00891735). DL regression models were developed to predict BCVA at the concurrent visit and 12 months from baseline using OCT images. Binary classification models were developed to predict BCVA of Snellen equivalent of <20/40, <20/60, and ≤20/200 at the concurrent visit and 12 months from baseline. Results: The regression model to predict BCVA at the concurrent visit had Conclusions: DL shows promise in predicting BCVA from OCTs in nAMD. Further research should elucidate the utility of models in clinical settings. Translational Relevance: DL models predicting BCVA could be used to enhance understanding of structure-function relationships and develop more efficient clinical trials.
Indexed as
Identifiers
What OpenQuestion holds
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.