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多模式人工智能模型使用电子健康记录和视网膜神经纤维层扫描预测玻璃眼的进展

Abigail Koornwinder¹, Youchen Zhang¹, Rohith Ravindranath¹

¹Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, USA.

Translational vision science & technology

|March 28, 2025

概括

通过结合电子健康记录 (EHR) 和视网膜纤维层光学连贯性断层扫描 (RNFL OCT) 扫描,可以更好地预测青光眼的手术需求. 结合两种数据类型的深度学习模型 (TabNet) 显示了对玻璃眼病进展预测的卓越性能.

科学领域:

眼科医生眼科眼科
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

玻璃眼是全球不可逆转的失明的主要原因.
准确预测青光眼的进展对于及时干预和个性化治疗至关重要.
当前的预测方法可能无法充分利用多式联运数据.

研究的目的:

开发和比较对玻璃眼外科手术进展的预测模型.
将结构化电子健康记录 (EHR) 数据与视网膜纤维层光学连贯性断层扫描 (RNFL OCT) 成像数据集成.
评估深度学习 (TabNet) 与传统机器学习 (XGBoost) 模型的性能.

主要方法:

从2008年到2023年分析了1472名青光眼患者的队列.
模型使用EHR数据,RNFL OCT扫描或两者的融合进行训练.
对 TabNet 和 XGBoost 架构进行了比较,以预测在 12 个月内进行手术.

主要成果:

塔布网融合模型实现了最高的预测性能 (AUROC = 0.832).
整合了EHR和RNFL数据的融合模型优于单模式模型.
深度学习TabNet架构的表现优于XGBoost模型.

结论:

整合多式联络数据 (EHR和RNFL OCT) 显著改善了对青光眼进展的预测.
深度学习模型,特别是TabNet,与XGBoost等传统方法相比,显示出更高的有效性.
这些预测模型可以支持针对个性化的玻璃眼管理的临床决策.

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