一个多模多任务的人工智能模型用于正眼镜的隐形眼镜配件:一个整合的框架来增强镜头集中和近视控制效果
Zhirong Zhang1,2,3,4, Xueyi Zhou1,2,3,4, Changjie Li5
1Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Ophthalmology science
|March 5, 2026
概括
这项研究开发了一个人工智能模型来分类角膜地形,并预测轴长度增长在 orthokeratology (ortho-K) 镜头佩戴者. 该模型准确地推了ortho-K透镜参数,帮助临床决定近视管理.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- орто-K 是一种控制近视的策略,使用刚性气体透透镜来重塑角膜.
- 准确预测轴长度 (AL) 增长和角膜地形对于有效的正视-K镜头安装和近视管理至关重要.
- 目前用于分析安装后的角膜地形和预测AL生长的方法可能是复杂和耗时的.
研究的目的:
- 开发和评估一种多模式,多任务的人工智能 (AI) 模型,用于对应角膜拓图案进行分类.
- 预测近视眼中轴长度 (AL) 的增长,这些眼睛正在接受正视K治疗.
- 为了同时输出最佳的ortho-K镜片参数和轴向增长的概率,以支持临床决策.
主要方法:
- 进行了对3529只装有ortho-K透镜的近视眼睛的临床数据和角膜拓图的回顾性分析.
- 构建了一个新的AI模型,包括基于ResNet50的回忆模型用于镜头参数预测和多式多任务排名模型 (比较ResNet50,ViT-B/16和CLIP-ViT) 用于地形分类和AL增长预测.
- 使用平均平方误差,回忆率和准确度来评估性能.
主要成果:
- 人工智能模型在分类后镜头角膜拓学 (0.95-0.96) 和预测轴向增长 (0.880-0.883) 中表现出高准确度.
- 80.0%的眼睛实现了理想的Class 1后镜头拓,在1年的随访中,62.79%的眼睛呈现了Class 1轴增长 (<0.3 mm/y).
- 该模型成功地推了最佳的ortho-K透镜参数,并预测了AL生长概率.
结论:
- 开发的多式联机多任务人工智能模型显示了改善正形-K镜头配件的重大前景.
- 人工智能模型通过准确地分类地形,预测AL增长和推镜头参数,提供了宝贵的决策支持.
- 这种人工智能方法可以提高临床效率和患者在近视治疗中获得的结果,使用ortho-K镜头.
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