ArticleAmerican journal of ophthalmology2021
Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.
Article in American journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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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
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 46 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Automated Assessment of OCT Angiography Image Quality Using the Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Data Set.Ophthalmology science · 2026Article
- Clinically aligned artificial intelligence for glaucoma diagnosis: enhancing retinal nerve fibre layer interpretation from fundus images.Eye (London, England) · 2026Article
- Stereo-Vision-Inspired Spectral Disparity Learning for Choroidal Tumor Thickness Prediction.Translational vision science & technology · 2026Article
- RNFL Thickness in a Population-Based Cohort: The Canadian Longitudinal Study on Aging M2M (Machine-to-Machine) Study.American journal of ophthalmology · 2026Article
- Augmented Decisions: AI-Enhanced Accuracy in Glaucoma Diagnosis and Treatment.Journal of clinical medicine · 2025Review
- A Deep Learning Model for Detecting the Eyes Receiving Glaucoma Medications Using Anterior Segment Images.Translational vision science & technology · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- Optimising deep learning models for ophthalmological disorder classification.Scientific reports · 2025Article
- Multi-step framework for glaucoma diagnosis in retinal fundus images using deep learning.Medical & biological engineering & computing · 2025Review
- Long-Term Rate of Optic Disc Rim Loss in Glaucoma Patients Measured From Optic Disc Photographs With a Deep Neural Network.Translational vision science & technology · 2024Article
- Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening.Journal of glaucoma · 2024Review
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.International journal of ophthalmology · 2024Article
- Predicting glaucoma progression using deep learning framework guided by generative algorithm.Scientific reports · 2023Article
- Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma.Translational vision science & technology · 2023Article
- Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State.International journal of molecular sciences · 2023Review
- The application of artificial intelligence in glaucoma diagnosis and prediction.Frontiers in cell and developmental biology · 2023Review
- Prediction of SMILE surgical cutting formula based on back propagation neural network.International journal of ophthalmology · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.
designRetrospective cohort study.
methodsThis study included 1,072 eyes of 827 glaucoma-suspect patients with an average follow-up of 5.9 ± 3.8 years. All eyes had normal standard automated perimetry (SAP) at baseline. Additional SAP and fundus photographs were acquired throughout follow-up. Conversion to glaucoma was defined as repeatable glaucomatous defects on SAP. An OCT-trained deep learning algorithm (machine to machine, M2M) was used to predict RNFL thicknesses from fundus photographs. Joint longitudinal survival models were used to assess whether baseline and longitudinal change in M2M's RNFL thickness estimates could predict development of visual field defects.
resultsA total of 196 eyes (18%) converted to glaucoma during follow-up. The mean rate of change in M2M's predicted RNFL thickness was -1.02 μm/y for converters and -0.67 μm/y for non-converters (P < .001). Baseline and rate of change of predicted RNFL thickness were significantly predictive of conversion to glaucoma, with hazard ratios in the multivariable model of 1.56 per 10 μm lower at baseline (95% CI, 1.33-1.82; P < .001) and 1.99 per 1 μm/y faster loss in thickness during follow-up (95% CI, 1.36-2.93; P < .001).
conclusionLongitudinal changes in a deep learning algorithm's predictions of RNFL thickness measurements based on fundus photographs can be used to predict risk of glaucoma conversion in eyes suspected of having the disease.
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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.