# Forecasting Future Humphrey Visual Fields Using Deep Learning

arXiv: https://arxiv.org/abs/1804.04543 · HTML: https://mlsanity.com/paper/1804.04543/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Multi-scale Spatio-temporal Transformer-based Imbalanced Longitudinal Learning for Glaucoma Forecasting from Irregular Time Series Images](https://mlsanity.com/paper/2402.13475/) (2024)
- [Artificial intelligence techniques in inherited retinal diseases: A review](https://mlsanity.com/paper/2410.09105/) (2024)
- [Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review](https://mlsanity.com/paper/2411.05876/) (2024)
- [Beyond Point Estimates for Glaucoma Visual Field Forecasting with Diffusion Models](https://mlsanity.com/paper/2606.30417/) (2026)
- [Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors](https://mlsanity.com/paper/2609.29256/) (2026)

## Abstract (arXiv)

Purpose: To determine if deep learning networks could be trained to forecast a future 24-2 Humphrey Visual Field (HVF). Participants: All patients who obtained a HVF 24-2 at the University of Washington. Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. Results: More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall MAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB). The 100 fully trained models were able to successfully predict progressive field loss in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF (p < 2.2 x 10 -16 ) and an average difference of 0.41 dB. Conclusions: Using unfiltered real-world datasets, deep learning networks show an impressive ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.