Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning.

ABSTRACT:

Background

The purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs.

Methods

We trained a DL model using a dataset of anterior segment photographs collected from Shanghai Aier Eye Hospital from June 2018 to December 2019. A Pentacam HR system was used to capture a 2D overview eye image and measure the ACD. Shallow ACD was defined as ACD less than 2.4 mm. The DL model was evaluated by a five-fold cross-validation test in a hold-out testing dataset. We also evaluated the DL model by testing it against two glaucoma specialists. The performance of the DL model was calculated by metrics, including accuracy, sensitivity, spec

SUBMITTER: Qian Z 

PROVIDER: S-EPMC8457334 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature