Primary angle-closed diseases recognition through artificial intelligence-based anterior segment-optical coherence tomography imaging.

IF 2.4 3区 医学 Q2 OPHTHALMOLOGY
Haipei Yao, Xiaolei Wang, Yan Suo, Jiangnan He, Chen Chu, Zhuozhen Yang, Qiuzhuo Xu, Jian Zhou, Mingqian Zhu, Xinghuai Sun, Ling Ge
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引用次数: 0

Abstract

Purpose: In this study, artificial intelligence (AI) was used to deeply learn the classification of the anterior segment-Optical Coherence Tomography (AS-OCT) images. This AI systems automatically analyzed the angular structure of the AS-OCT images and automatically classified anterior chamber angle. It would improve the efficiency of AS-OCT image analysis.

Methods: The subjects were from the glaucoma disease screening and prevention project for elderly people in Shanghai community. Each scan contained 72 cross-sectional AS-OCT frames. We developed a deep learning-based AS-OCT image automatic anterior chamber angle analysis software. Classifier performance was evaluated against glaucoma experts' grading of AS-OCT images as standard. Outcome evaluation included accuracy (ACC) and area under the receiver operator curve (AUC).

Results: 94895 AS-OCT images were collected from 687 participants, in which 69,243 images were annotated as open, 16,433 images were annotated as closed, and 9219 images were annotated as non-gradable. The class-balanced train data were formed from randomly extracting the same number of open angle images as the closed angle images, which contained 22,393 images (11127 open, 11256 closed). The best-performing classifier was developed by applying transfer learning to the ResNet-50 architecture. against experts' grading, this classifier achieved an AUC of 0.9635.

Conclusion: Deep learning classifiers effectively detect angle closure based on automated analysis of AS-OCT images. This system could be used to automate clinical evaluations of the anterior chamber angle and improve efficiency of interpreting AS-OCT images. The results demonstrated the potential of the deep learning system for rapid recognition of high-risk populations of PACD.

通过基于人工智能的前段光学相干断层成像识别原发性闭角型疾病。
目的:本研究利用人工智能(AI)深度学习前段光学相干断层扫描(AS-OCT)图像的分类。该人工智能系统可自动分析 AS-OCT 图像的角度结构,并自动对前房角度进行分类。这将提高 AS-OCT 图像分析的效率:方法:研究对象来自上海社区老年人青光眼疾病筛查和预防项目。方法:研究对象来自上海社区老年人青光眼疾病筛查预防项目,每次扫描包含72个横断面AS-OCT帧。我们开发了基于深度学习的AS-OCT图像前房角膜自动分析软件。以青光眼专家对AS-OCT图像的分级为标准,对分类器的性能进行评估。结果评估包括准确率(ACC)和接收者运算曲线下面积(AUC):共收集了 687 名参与者的 94895 张 AS-OCT 图像,其中 69243 张图像被标注为开放,16433 张图像被标注为闭合,9219 张图像被标注为不可分级。类平衡训练数据是通过随机提取与闭角图像数量相同的开角图像形成的,其中包含 22,393 幅图像(11127 幅开角图像,11256 幅闭角图像)。通过将迁移学习应用于 ResNet-50 架构,开发出了表现最佳的分类器。根据专家的评分,该分类器的 AUC 达到了 0.9635:基于对 AS-OCT 图像的自动分析,深度学习分类器能有效检测闭角。该系统可用于自动进行前房角的临床评估,并提高解读 AS-OCT 图像的效率。结果表明,深度学习系统具有快速识别 PACD 高危人群的潜力。
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来源期刊
CiteScore
5.40
自引率
7.40%
发文量
398
审稿时长
3 months
期刊介绍: Graefe''s Archive for Clinical and Experimental Ophthalmology is a distinguished international journal that presents original clinical reports and clini-cally relevant experimental studies. Founded in 1854 by Albrecht von Graefe to serve as a source of useful clinical information and a stimulus for discussion, the journal has published articles by leading ophthalmologists and vision research scientists for more than a century. With peer review by an international Editorial Board and prompt English-language publication, Graefe''s Archive provides rapid dissemination of clinical and clinically related experimental information.
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