时间和人敏感的基础模型用于疾病预测和风险分层
Zheyuan Wang1,2, Yukun Zhou3,4,5, Yilan Wu3,6
1Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
NPJ digital medicine
|March 15, 2026
概括
新的人工智能模型RETFound Plus使用视网膜图像增强了对眼睛和全身疾病的预测. 这种先进的基础模型 (FM) 改善了对中风和糖尿病等疾病的5年风险预测和分层.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 现有的视网膜基础模型 (FMs) 在分类和检测方面表现出色,但在预测疾病发生率和进展方面存在局限性.
- 需要人工智能模型能够从纵向视网膜数据中学习时间模式,以预测疾病轨迹.
研究的目的:
- 开发和评估RETFound Plus,一个新的基础模型 (FM),旨在从纵向视网膜底部照片中进行进展意识的表示学习.
- 与现有模型相比,评估RETFound Plus在预测5年风险和改善系统性和眼部疾病风险分层方面的表现.
主要方法:
- 在多次访问中训练了一种基于CFP的FM (RETFound Plus),使用时间建模对来自304,345名参与者的1,304,292张 fundus照片进行时间建模.
- 评估模型校准,五年风险预测 (c指数) 和风险分层 (危险比率趋势) 系统性 (中风,心肌梗塞,糖尿病,高血压) 和眼部疾病 (糖尿病视网膜病变,玻璃眼瘤).
- 验证了来自英国,美国,新加坡,香港和丹麦的多元,多区域和多民族外部数据集的结果.
主要成果:
- 与基线RETFound模型相比,RETFound Plus显示了改进的校准和5年风险预测.
- 系统性结局 (中风,心肌梗塞,糖尿病,高血压;+4-10%c指数) 的表现收益比眼部结局 (糖尿病视网膜病变,绿斑;+3-7%c指数) 的表现收益更大.
- RETFound Plus显著改善了全身性疾病的风险分层,显示出1.2-2.1倍高的危险比率趋势,在外部验证数据集中表现一致.
结论:
- 时间基础模型RETFound Plus有效地从纵向视网膜图像中学习进展意识表示.
- 该模型显示了预测和分层对系统性和眼部疾病风险的增强能力,特别是系统性疾病.
- RETFound Plus在不同的人群和地区的稳定性和通用性突显了其在疾病预测中广泛临床应用的潜力.
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