AMD-Mamba:一种表型感知多模式框架,用于稳健的AMD预后
Puzhen Wu1, Mingquan Lin2, Qingyu Chen3
1Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10022, USA.
ArXiv
|September 19, 2025
概括
我们开发了AMD-Mamba,这是一个新的AI框架和生物标志物,用于预测与年龄相关的黄斑变性 (AMD) 进展. 该工具整合了成像,遗传和人口统计数据,以便更早地检测高风险患者.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 遗传学 遗传学 是一个
背景情况:
- 与年龄相关的黄斑变性 (AMD) 是不可逆转的视力丧失的主要原因.
- 准确的AMD预后对于及时的临床干预至关重要.
- 现有的预测模型往往缺乏全面的数据集成和高级特征提取.
研究的目的:
- 介绍AMD-Mamba,这是一个用于AMD预后的新型多模式框架.
- 开发和验证一个新的AMD生物标志物,以改善疾病进展的预测.
- 为了提高高风险AMD患者的早期检测.
主要方法:
- 开发了AMD-Mamba,这是一个集色底图像,遗传变异和社会人口统计数据的多模式框架.
- 采用了一种新的度量学习策略,使用AMD严重程度等级得分来实现更丰富的特征表示.
- 利用Vision Mamba用于融合本地和全球信息提取,以及图像和临床数据的多尺度融合.
主要成果:
- 拟议的AMD生物标志物证明了AMD进展的显著预测能力.
- 在与现有变量相结合时,AMD-Mamba在早期阶段可以更好地检测高风险的AMD患者.
- 对AREDS数据集的实验验证 (45,818张图像,52个遗传变异,3个来自2,741名受试者的社会人口学变量) 证实了该框架的有效性.
结论:
- AMD-Mamba为精确的AMD预后提供了一个有前途的多模式方法.
- 这种新生物标志物有助于识别患AMD进展高风险的个体.
- 这一框架促进了与年龄相关的黄斑退化症的积极和个性化管理策略.
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