Automatic Classification of Anterior Chamber Angle Using Ultrasound Biomicroscopy and Deep Learning.

ABSTRACT:

Purpose

To develop a software package for automated classification of anterior chamber angle of the eye by using ultrasound biomicroscopy.

Methods

Ultrasound biomicroscopy images were collected, and the trabecular-iris angle was manually measured and classified into three categories: open angle, narrow angle, and angle closure. Inception v3 was used as the classifying convolutional neural network and the algorithm was trained.

Results

With a recall rate of 97% in the test set, the neural network's classification accuracy can reach 97.2% and the overall area under the curve was 0.988. The sensitivity and specificity were 98.04% and 99.09% for the open angle, 96.30% and 98.13% for the narrow angle, and 98.21% and 99.05% for the angle closure categories, respectively.

SUBMITTER: Shi G 

PROVIDER: S-EPMC6703191 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature