Deep Learning Evaluation of Glaucoma Detection Using Fundus Photographs in Highly Myopic Populations.

ABSTRACT:

Objectives

This study aimed to use deep learning to identify glaucoma and normal eyes in groups with high myopia using fundus photographs.

Methods

Patients who visited Tri-Services General Hospital from 1 November 2018 to 31 October 2022 were retrospectively reviewed. Patients with high myopia (spherical equivalent refraction of ≤-6.0 D) were included in the current analysis. Meanwhile, patients with pathological myopia were excluded. The participants were then divided into the high myopia group and high myopia glaucoma group. We used two classification models with the convolutional block attention module (CBAM), an attention mechanism module that enhances the performance of convolutional neural networks (CNNs), to investigate glaucoma cases. The learning data of this experi

SUBMITTER: Chiang YY 

PROVIDER: S-EPMC11274657 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature