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基于深度学习的预测近视控制效应在儿童治疗过夜的ortokeratology治疗

Jingwen Cao¹, Xiaoming Sun, Lu Sun

¹Key Laboratory of Universal Wireless Communications (J.C., K.N., Z.H.), Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China; Department of Ophthalmology (X.S.), the PLA General Hospital, Beijing, China; and Beijing Tongren Eye Center (L.S., H.S.), Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and Visual Sciences, National Engineering Research Center for Ophthalmology, Beijing, China.

Eye & contact lens

|November 7, 2023

概括

一个深度学习模型准确地预测了接受脊髓瘤学 (Ortho-K) 治疗的儿童的轴长度 (AL) 延伸. 该模型使用基线因素和角膜地形,帮助近视控制策略.

科学领域:

眼科医生眼科眼科
生物医学工程生物医学工程
医疗保健中的人工智能

背景情况:

整形眼镜治疗 (Ortho-K) 是一种使用刚性气体透透镜的近视控制方法.
预测轴长度 (AL) 延长对于管理儿童近视进展至关重要.
早期识别影响AL延长的因素可以优化治疗结果.

研究的目的:

开发和验证一种深度学习模型,用于预测儿科Ortho-K患者的12个月AL延长.
为了确定与AL延长相关的基线因素和早期角膜地形变化.
探索这些预测因素与近视控制的有效性之间的联系.

主要方法:

分析了115名儿科Ortho-K患者的队列.
使用皮尔森相关系数确定与AL延长相关的基线因子.
结合多重线性回归和深度神经网络的预测模型被开发和验证在独立的患者群体.

主要成果:

年龄,球体等效折射误差 (SE) 和性别是AL延长的显著预测因素 (P <0.05).
角膜的地形变化,包括失焦区域的高度,面积和体积被纳入模型.
经过验证的深度学习模型在预测12个月的AL延长方面表现出很高的准确性.

结论:

一种深度学习模型有效地预测了儿科Ortho-K患者的轴长度延长.
该模型整合了基线患者数据和角膜地形变化,以准确预测.
这种预测工具可以增强近视管理策略,并个性化Ortho-K治疗.

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