Automatic evaluation system for ultrasound biomicroscopy images of anterior chamber angle based on deep learning algorithm
10.3980/j.issn.1672-5123.2023.5.23
- VernacularTitle:基于深度学习算法的前房角超声生物显微镜图像自动评估系统
- Author:
Wei-Yan JIANG
1
;
Yu-Lin YAN
1
;
Si-Min CHENG
1
;
Yan-Ning YANG
1
Author Information
1. Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, China
- Publication Type:Journal Article
- Keywords:
ophthalmic ultrasound biomicroscopy;
artificial intelligence;
deep learning;
primary angle-closure glaucoma;
anterior chamber angle;
scleral spur
- From:
International Eye Science
2023;23(5):833-842
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To explore the clinical application value of analysis system for ultrasound biomicroscopy(UBM)images of anterior chamber angle(ACA)based on deep learning algorithm.METHODS: A total of 4 196 UBM images were obtained from 675 patients(1 130 eyes)at the Eye Center of Renmin Hospital of Wuhan University from January 2021 to June 2022 were collected to build an image dataset. Using Unet++network to automatically segment ACA tissue, a support vector machine(SVM)algorithm was developed to automatically classify opening and closing of chamber angle, and an algorithm to automatically locate the sclera spur and measure ACA parameters was developed. Furthermore, a total of 631 UBM images of 127 subjects(221 eyes)at Huangshi Aier Eye Hospital and 594 UBM images of 188 subjects(257 eyes)at Zhongnan Hospital of Wuhan University were selected to evaluate the performance of the system under different environments.RESULTS: The accuracy of the analysis system constructed in this study for chamber angle opening and closing was 95.71%. The intra-class correlation coefficient(ICC)values of all ACA angle parameters were greater than 0.960. ICC values of all ACA thickness parameters were greater than 0.884. The accurate measurement of ACA parameters depended in part on the accurate location of the scleral spur.CONCLUSION: The intelligent analysis system constructed in this study can accurately and effectively evaluate ACA images automatically and is a potential screening tool for the rapid identification of ACA structures.
