Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.

ABSTRACT:

Purpose

To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure-function mapping.

Design

Retrospective, cross-sectional database study.

Participants

A total of 6437 patients undergoing routine care for glaucoma in 3 clinical sites in the United Kingdom.

Methods

OCT and infrared reflectance (IR) optic disc imaging were paired with the closest VF within 7 days. EfficientNet B2 was used to train 2 single-modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the 2 model predictions.

Main outcome measures

Pointwise mean absolute error (PMAE).

Results

A total of 5078 imaging sc

SUBMITTER: Kihara Y 

PROVIDER: S-EPMC9233104 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature