Application of Artificial Intelligence and Deep Learning for Choroid Segmentation in Myopia.

Purpose: To investigate the correlation between choroidal thickness and myopia progression using a deep learning method.

Methods: Two data sets, data set A and data set B, comprising of 123 optical coherence tomography (OCT) volumes, were collected to establish the model and verify its clinical utility. The proposed mask region-based convolutional neural network (R-CNN) model, trained with the pretrained weights from the Common Objects in Context database as well as the manually labeled OCT images from data set A, was used to automatically segment the choroid.

Automatic Segment and Quantify Choroid Layer in Myopic eyes: Deep Learning based Model.

Purpose: To report a rapid and accurate method based upon deep learning for automatic segmentation and measurement of the choroidal thickness (CT) in myopic eyes, and to determine the relationship between refractive error (RE) and CT.

Methods: Fifty-four healthy subjects 20-39 years of age were retrospectively reviewed. Data reviewed included age, gender, laterality, visual acuity, RE, and Enhanced Depth Imaging Optical Coherence Tomography (EDI-OCT) images.