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基于视网膜的生物年龄在评估慢性阻塞性肺病风险方面的预测潜力

Qingsheng Peng^1,2, Tyler Hyungtaek Rim^3,4, Zhi Da Soh^1,5

¹Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.

Clinical & experimental ophthalmology

|February 7, 2025

概括

视网膜衰老预测 (RetiAGE) 对识别患慢性阻塞性肺病 (COPD) 风险较高的个体有希望. 这种深度学习工具可能有助于在临床环境中分层化COPD风险.

科学领域:

眼科医生眼科眼科
肺部病理学肺部病理学
人工智能的人工智能

背景情况:

之前开发了一种深度学习算法RetiAGE,用于从视网膜照片中预测生物年龄.
RetiAGE已被证明与未来的发病率和死亡率风险有关.

研究的目的:

评估RetiAGE在预测慢性阻塞性肺病 (COPD) 未来风险方面的表现.

主要方法:

RetiAGE评分是从英国生物库参与者的视网膜图像中生成的.
考克斯的比例危险模型评估了RetiAGE和COPD事件之间的关联,并对混因素进行了调整.
通用线性模型检查了RetiAGE与基线呼吸功能 (FEV1/FVC,PEF) 之间的关联.

主要成果:

中度和高RetiAGE风险三角体的参与者具有较低的基线呼吸功能 (p <0.05).
较高的RetiAGE风险三角动物与发生COPD的风险显著增加有关 (HR = 1.60).
纳入RetiAGE改善了多变量风险模型的预测性能 (p < 0.001).

结论:

基于深度学习的视网膜衰老生物标志物RetiAGE显示了对COPD发展风险分层的潜力.

关键词:

慢性慢性肺炎是一种慢性慢性肺炎,COPD是一种慢性肺炎.深度学习是一种深度学习.肺功能肺功能肺功能视网膜衰老视网膜衰老视网膜摄影视网膜摄影

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