Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning.

ABSTRACT:

Purpose

To assess the performance of a perimetric strategy using structure-function predictions from a deep learning (DL) model.

Methods

Visual field test-retest data from 146 eyes (75 patients) with glaucoma with (median [5th-95th percentile]) 10 [7, 10] tests per eye were used. Structure-function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Ground-truth sensitivities for each eye were the medians of the test

SUBMITTER: Montesano G 

PROVIDER: S-EPMC10587851 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature