Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning.

ABSTRACT:

Purpose

The purpose of this study was to engineer deep learning (DL) models that can identify myopic maculopathy in patients with high myopia based on optical coherence tomography (OCT) images.

Methods

An artificial intelligence (AI) system was developed using 2342 qualified OCT macular images from 1041 patients with pathologic myopia admitted to the Affiliated Eye Hospital of Wenzhou Medical University (WMU). We adopted an ResNeSt101 architecture to train five independent models to identify the following five myopic maculopathies: macular choroidal thinning, macular Bruch membrane (BM) defects, subretinal hyper-reflective material (SHRM), myopic traction maculopathy (MTM), and dome-shaped macula (DSM). We tested the models with an independent test dataset that included 450

SUBMITTER: Ye X 

PROVIDER: S-EPMC8590175 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature