基于深度学习的图像质量评估用于光学连贯性断层扫描 黄斑扫描:一个多中心研究.
Ziqi Tang1, Xi Wang2,3, An Ran Ran1
1Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
The British journal of ophthalmology
|July 20, 2024
概括
研究人员开发了深度学习模型,以评估3D光学连贯性断层扫描 (3D optical coherence tomography) 和黄斑扫描的图像质量. 这些模型有效地过了不可分类的扫描,从而实现了自动化眼病检测工作流程.
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:
- 精确评估光学连贯断层扫描 (OCT) 图像质量对于可靠的诊断至关重要.
- 深度学习 (DL) 提供了在视网膜成像中自动化图像质量评估的潜力.
研究的目的:
- 开发和外部验证深度学习模型,以评估Cirrus和Spectralis OCT设备3D黄斑扫描的图像质量.
- 评估这些DL模型在来自不同国际来源的未见数据集上的性能.
主要方法:
- 2277个Cirrus和1557个Spectralis 3D OCT扫描的回顾性收集用于培训,微调和内部验证.
- 使用了Cirrus的3D剩余网络 (ResNet) -18和Spectralis的ResNet-18的多实例学习管道.
- 外部测试涉及来自多个国家的三个未见的Cirrus数据集和五个未见的Spectralis数据集.
主要成果:
- 内部验证显示了高性能,曲线下面积 (AUC) 为Cirrus扫描的0.930和Spectralis扫描的0.906.
- 外部测试证实了强大的性能,Cirrus的AUC从0.832到0.930不等,Spectralis的AUC从0.891到0.962.
结论:
- 开发的DL模型可以有效地识别和过不可分类的3D OCT黄斑扫描.
- 将这些模型集成到疾病检测工作流中可以促进完全自动化的眼病检测.
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