Dataset Information

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.

Conclusions

Preliminary results show that an automated classification of the anterior chamber angle achieved satisfying sensitivity and specificity and could be helpful in clinical practice.

Translational relevance

The present work suggests that the algorithm described here could be useful in the categorizing of anterior chamber angle and screening for subjects who are at high risk of angle closure.

SUBMITTER: Shi G

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

REPOSITORIES: biostudies-literature

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Publications

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

Shi Guohua G Jiang Zhenying Z Deng Guohua G Liu Guangxing G Zong Yuan Y Jiang Chunhui C Chen Qian Q Lu Yi Y Sun Xinhuai X

Translational vision science & technology 20190701 4

<h4>Purpose</h4>To develop a software package for automated classification of anterior chamber angle of the eye by using ultrasound biomicroscopy.<h4>Methods</h4>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.<h4>Results</h4>With a recall rate of 97% in the test s ...[more]

PMID: 31448182

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Dataset Information

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

Purpose

Methods

Results

Conclusions

Translational relevance

Publications

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

Similar Datasets

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