Estimating Retinal Sensitivity Using Optical Coherence Tomography With Deep-Learning Algorithms in Macular Telangiectasia Type 2.

ABSTRACT:

Importance

As currently used, microperimetry is a burdensome clinical testing modality for testing retinal sensitivity requiring long testing times and trained technicians.

Objective

To create a deep-learning network that could directly estimate function from structure de novo to provide an en face high-resolution map of estimated retinal sensitivity.

Design, setting, and participants

A cross-sectional imaging study using data collected between January 1, 2016, and November 30, 2017, from the Natural History Observation and Registry of macular telangiectasia type 2 (MacTel) evaluated 38 participants with confirmed MacTel from 2 centers.

Main outcomes and measures

Mean absolute error of estimated compared with observed retinal sensitivity. Observed retinal sensi

SUBMITTER: Kihara Y 

PROVIDER: S-EPMC6484597 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature