多式人工智能用于预测高近视期近视冠状腺新血管化的3年和5年风险
Yining Wang1, Takashi Ishida2, Ziye Wang1
1Department of Ophthalmology and Visual Science, Institute of Science Tokyo, Tokyo, Japan.
Ophthalmology. Retina
|February 8, 2026
概括
人工智能 (AI) 模型现在可以估计近视性黄斑新血管化 (MNV) 在高近视的风险. 这些人工智能工具为长期监测策略提供了临床上有意义的风险分层.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 高近视性对发展近视性黄斑新血管化 (MNV) 具有显著的风险.
- 准确的风险预测对于及时干预和高近视 MNV 的管理至关重要.
- 当前的风险评估方法可能无法完全捕捉到MNV发展的复杂性.
研究的目的:
- 开发和验证基于人工智能 (AI) 的模型,用于估计高度近视眼睛中MNV的风险.
- 评估基于图像的深度学习 (DL) 模型和多模式生存模型的预测性能.
- 探索人工智能模型对MNV风险分层的临床实用性和可解释性.
主要方法:
- 追溯队列研究涉及2501名高近视患者的4235只眼睛.
- 使用基底照片 (DenseNet-121) 开发三种DL模型,用于1年,3年和5年的MNV风险预测.
- 创建一个多模式的DeepSurv模型,集成图像特征和临床变量,用于时间到事件分析.
主要成果:
- 在DL模型中,接收器运行特征曲线 (AUROC) 下的面积在不同的预测视界中从0.729到0.798不等.
- 多式联网DeepSurv模型显示一致性指数 (C指数) 为0.683,从而实现了有效的风险分层.
- SHAP分析强调了轴长和病理近视类别作为多式模式中的关键预测因素.
结论:
- 基于人工智能的模型提供了对高近视的MNV的准确的固定视界和时间到事件风险估计.
- 这些模型提供了临床上有意义的风险分层,可能支持量身定制的长期监测.
- 建议进一步进行外部验证,以支持对高近视管理的AI方法的临床实施.
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