Diagnostic Performance of Deep Learning Classifiers in Measuring Peripheral Anterior Synechia Based on Swept Source Optical Coherence Tomography Images.

ABSTRACT:

Purpose

To develop deep learning classifiers and evaluate their diagnostic performance in detecting the static gonioscopic angle closure and peripheral anterior synechia (PAS) based on swept source optical coherence tomography (SS-OCT) images.

Materials and methods

Subjects were recruited from the Glaucoma Service at Zhongshan Ophthalmic Center of Sun Yat-sun University, Guangzhou, China. Each subject underwent a complete ocular examination, such as gonioscopy and SS-OCT imaging. Two deep learning classifiers, using convolutional neural networks (CNNs), were developed to diagnose the static gonioscopic angle closure and to differentiate appositional from synechial angle closure based on SS-OCT images. Area under the receiver operating characteristic (ROC) curve (AUC) was use

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8825342 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature