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通过成像照明对灯照片进行自动分类

Ming-Chen Lu¹, Callie Deng¹, Miles F Greenwald¹

¹Department of Ophthalmology and Visual Sciences, School of Medicine, University of Michigan, Ann Arbor, MI.

|June 2, 2023

概括

这项研究开发了一种使用深度学习的自动算法,以基于状炎诊断的照明技术来分类裂灯照片 (SLPs). 移动NetV2模型在从角膜图像中识别照明类型方面取得了很高的准确性.

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:

角膜炎的准确诊断依赖于对角膜图像的详细检查.
在裂纹灯照片 (SLP) 中对照明技术进行分类对于诊断角膜炎至关重要.
手动图像注释是耗时的,容易变化.

研究的目的:

开发一种基于照明技术的自动算法来对SLP进行分类.
为了促进深度学习系统在图像注释中进行炎诊断.
通过自动化图像分析,提高炎诊断的效率和准确性.

主要方法:

从409名患有角膜的患者中收集了12132个SLP.
根据四种照明技术进行分类图像:裂纹光束,扩散白光,光素扩散蓝光和硬化散射 (ScS).
训练和评估各种分类算法,包括MobileNetV2,使用70%:15%:15%的数据分割.

主要成果:

移动NetV2表现出卓越的性能,整体F1得分为97.95%,AUC-ROC为99.83%,准确度为98.98%.
所有照明类型都获得了高F1得分,分散的蓝光显示最高的99.88%.
裂纹光束和SCS是最常被错误分类的照明技术.

结论:

移动NetV2算法使用大量数据集准确地对SLP中的照明技术进行分类.
对SLP的自动分类对于将深度学习整合到临床决策支持中至关重要.
这项技术可以改善诊断角膜炎的临床工作流程.

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