通过基于人工智能的前段光学连贯性断层扫描成像识别主要的闭角疾病.
Haipei Yao1, Xiaolei Wang2, Yan Suo1
1Department of Ophthalmology, Shanghai Eye Diseases Prevention &Treatment Center/ Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
概括
人工智能 (AI) 深度学习有效地从前段-光学连贯断层扫描 (AS-OCT) 图像中对前腔角进行分类. 这种自动化分析提高了检测闭角眼风险的效率.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 前段光学一致性断层扫描 (AS-OCT) 对于评估前腔角度至关重要.
- 自动分析AS-OCT图像可以提高诊断效率.
- 玻璃眼查和预防工作从改进的成像分析中受益.
研究的目的:
- 开发和评估人工智能系统,使用AS-OCT图像自动分类前腔角度.
- 提高AS-OCT图像分析在临床环境中的效率和准确性.
- 通过自动化图像分析,识别高风险患初级角闭眼光眼 (PACG) 的个体.
主要方法:
- 一个深度学习模型是使用AS-OCT图像从眼查项目开发的.
- 该数据集包括687名参与者的94,895张AS-OCT图像.
- 对分类器的性能进行了评估,对比专家的分级,使用准确度和接收机操作员曲线 (AUC) 下的面积.
主要成果:
- 基于转移学习的ResNet-50架构的表现最好的分类器实现了0.9635.5的AUC.
- 该系统在区分开放和关闭前腔室角度方面表现出高精度.
- 一个包含22,393张图像的类平衡数据集被用于训练分类器.
结论:
- 深度学习分类器可以有效地检测AS-OCT图像的角度闭合.
- 开发的AI系统自动化了前腔室角度的临床评估,提高了效率.
- 这项技术显示出迅速识别PACG高风险种群的潜力.
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