多模式深度学习用于使用顺序RNFL,视觉场和临床数据长度预测青光眼进展
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
准确预测青光眼的进展对于预防视力丧失至关重要. 一个新的深度学习模型整合了眼部扫描和临床数据,以预测多年来疾病的进展.
科学领域:
- 眼科和人工智能的人工智能
- 医学成像和机器学习
背景情况:
- 预测青光眼的进展对于预防不可逆转的视力损失至关重要.
- 现有的方法在长期预测准确性和多式联运数据集成方面扎.
研究的目的:
- 开发和验证一个多式,纵向深度学习框架,用于预测青光眼的进展.
- 使用综合的患者数据,预测疾病在两到四年的时间里进展.
主要方法:
- 利用了10864名青光眼患者的回顾性队列.
- 集成的顺序结构 (OCT RNFL扫描),功能 (视野图) 和临床数据.
- 采用了四个深度学习骨干架构 (ConvNeXt-V2,ViT,MobileNet-V2,EfficientNet-B0) 与一个双向的LSTM进行时间分析.
主要成果:
- 基于ConvNeXt-V2的模型实现了高性能 (0.97 AUC,准确度为0.94-0.96).
- 在不同的人口分组 (性别,种族) 中表现出强的表现,对老年人 (>70岁) 的影响最小.
- 度图显示了临床相关性,将预测定位在相关的视网膜神经纤维层区域.
结论:
- 开发的深度学习框架有效地融合了多模式数据,以实现准确的,长期的玻璃眼瘤进展风险分层.
- 该模型提供了可解释和公平的预测,推进了个性化的玻璃眼管理策略.
- 这种方法具有显著的潜力,可以在青光眼患者中进行主动干预和维护视力.
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