ArticlePloS one2024
Bidirectional gated recurrent unit network model can generate future visual field with variable number of input elements.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Predicting worsening visual field via masked dual stage attention recurrent neural network.Scientific reports · 2026Article
- A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.NPJ digital medicine · 2026Article
- Functional generalized estimating equation model to detect glaucomatous visual field progression.International journal of ophthalmology · 2026Article
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Authors and funding
8 authors.
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Abstract
purposeThis study aimed to predict future visual field tests using a bidirectional gated recurrent unit (Bi-GRU) and assess its performance based on the number of input visual field tests and the prediction time interval. MATERIALS AND
methodsThis study included patients who underwent visual field tests at least four times at five university hospitals between June 2004 and April 2022. All data were accessed in October 2022 for research purposes. In total, 23,517 eyes with 185,858 visual field tests were used as the training dataset, and 1,053 eyes with 9,459 visual field tests were used as the test dataset. The Bi-GRU architecture was designed to take a variable number of visual field tests, ranging from 3 to 80, as input and predict visual field tests at the desired arbitrary time point. It generated the mean deviation (MD), pattern standard deviation (PSD), Visual Field Index (VFI), and total deviation value (TDV) for 54 test points. To analyze the model performance, the mean absolute error between the actual and predicted values was calculated and analyzed for glaucoma severity, number of input visual field tests, and prediction time interval.
resultsThe prediction errors of the Bi-GRU model for MD, PSD, VFI, and TDV ranged from 1.20 to 1.68 dB, 0.95 to 1.16 dB, 3.64 to 4.51%, and 2.13 to 2.60 dB, respectively, depending on the number of input visual field tests. Prediction errors tended to increase as the prediction time interval increased; however, the difference was not statistically significant. As the severity of glaucoma worsened, the prediction errors significantly increased.
conclusionIn clinical practice, the Bi-GRU model can predict future visual field tests at the desired time points using three or more previous visual field tests.
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