ReviewJapanese journal of ophthalmology2023
Prediction of visual field progression in glaucoma: existing methods and artificial intelligence.
Review in Japanese journal of ophthalmology, 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.
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Who cites it
7 citing papers in PubMed.
- Multimodal Deep Learning for Humphrey 24-2 Visual Field Prediction: Evaluating the Contribution of Fellow-Eye Information in Glaucoma and Ocular Hypertension.Journal of clinical medicine · 2026Article
- Future visual field prediction in glaucoma: an application of first-order autoregressive approach.International ophthalmology · 2026Article
- Toward Long-Term Visual Field Appearance Forecasting Using Artificial Intelligence for Ophthalmic Education and Diagnosis.Ophthalmology science · 2026Article
- Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.Ophthalmology science · 2026Article
- Visual field prediction using K-means clustering in patients with primary open angle glaucoma.International journal of ophthalmology · 2026Article
- Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.Japanese journal of ophthalmology · 2025Observational
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.
Indexed as
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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.