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通过深度学习对慢性病进行查,利用超广场底图像

Xinyu Zhao¹, Xingwang Gu¹, Lihui Meng¹

¹Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China and Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, China.

NPJ digital medicine

|October 7, 2024

概括

一种新的深度学习模型,UWF-CKDS,使用超广场底部图像进行精确的慢性病 (CKD) 查. 这种方法表现出卓越的性能,为人口级CKD检测提供了可扩展的解决方案.

科学领域:

眼科医生眼科眼科
腎臟病學 (nephrology) 是一種醫學.
人工智能的人工智能

背景情况:

慢性病 (CKD) 是一个重大的全球健康挑战.
目前的CKD查方法在可扩展性和可访问性方面面临限制.
视网膜成像为系统健康提供了潜在的非侵入性窗口,包括功能.

研究的目的:

开发和验证使用超广场 (UWF) 底部图像进行CKD查的深度学习模型.
通过分析视网膜微血管参数 (RMPs) 来提高模型的解释性.
将基于UWF的模型的性能与仅使用中枢视网膜图像的模型进行比较.

主要方法:

开发了一个深度学习模型,UWF-CKDS,用于从UWF fundus图像中预测CKD.
视网膜血管和RMP被提取用于与功能相关性分析.
该模型使用来自中国23家三级医院的数据进行了验证.
性能与CTR-CKDS进行了比较,CTR-CKDS是一种使用中枢视网膜图像的模型.

主要成果:

通过整合UWF图像,RMP和病史,UWF-CKDS准确地确定了CKD状态.
在功能和RMP之间发现了显著的相关性,提高了模型的解释性.
与CTR-CKDS相比,UWF-CKDS表现出更高的性能.
突出了外围视网膜对预测功能的贡献.

结论:

UWF-CKDS是一个高度可实现的深度学习工具,用于大规模CKD查.
该模型利用UWF fundus成像和RMP进行准确和高效的CKD检测.
这种方法为人口层面的查和早期识别CKD提供了一个有希望的策略.

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