基于深度学习的评估媒体雾和视网膜血管泄漏的尿道炎
Zixiang Wang1, Hao Liang2, Mali Dai3
1National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
Ocular immunology and inflammation
|October 22, 2025
概括
深度学习模型使用CFP和FFA图像准确地评估脑膜炎介质雾和血管泄漏. 这种自动化方法可以提高诊断准确度,并为疾病活动和治疗反应提供新的指标.
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:
- 尿道炎的诊断和监测通常依赖于媒体雾和血管泄漏的主观评估.
- 眼部成像的自动化分析可以提高诊断一致性和效率.
研究的目的:
- 使用CFP和FFA开发和评估深度学习模型,用于自动评估CFP和FFA使用的脑膜炎中介质雾和血管泄漏.
- 将深度学习模型的性能与传统方法进行比较.
主要方法:
- 开发了EfficientNetV2-L,InceptionV3和MobileNetV3用于媒体雾评估,以及LadderNet用于血管和泄漏细分.
- 利用了来自213名脑膜炎患者的756张CFP和740张FFA图像.
- 进行了相关性分析和K-means集群,以识别泄漏模式.
主要成果:
- 在媒体雾分类方面,EfficientNetV2-L实现了高性能 (微AUC为0.933).
- LadderNet在血管 (DSC 0.95) 和泄漏 (DSC 0.89) 细分方面表现出高精度.
- 发现媒体雾,泄漏和中性粒细胞与淋巴细胞比率 (NLR) 之间存在显著的关联,并观察到治疗后的改善.
结论:
- 深度学习显示了自动化膜炎诊断和评估的巨大潜力.
- 开发的模型提供了更高的准确性和对疾病活动和治疗疗效的新见解.
- 自动化分析可以为监测疾病进展和治疗结果提供有价值的指标.
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