TL;DR
This study demonstrates that deep learning models can accurately forecast future Humphrey Visual Fields up to 5.5 years ahead using only a single baseline test, aiding glaucoma progression prediction.
Contribution
Introduces a deep learning approach capable of predicting future visual fields from a single baseline, advancing glaucoma progression modeling.
Findings
Best model achieved 2.47 dB MAE on test set.
Predicted visual fields correlated with actual with r=0.92 up to 5.5 years.
Model trained on over 1.7 million data points from 32,443 tests.
Abstract
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…
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