预测EVO-ICL金库:一个数据处理框架,集成多中心大数据和机器学习
Xiaoli Li1,2, Hongbin Lin3, Guangzhong Fan4
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Seven Jinsui Road, Guangzhou, 510060, People's Republic of China.
Ophthalmology and therapy
|December 9, 2025
概括
机器学习模型使用新型数据纠纷系统 (DVIS) 预测可植入的Collamer镜头 (ICL) 顶. 这种方法提高了预测的准确性,帮助ICL手术的临床决策.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 准确预测可植入的科拉默透镜 (ICL) 密室对于成功的折射手术结果至关重要.
- 由于数据的复杂性和可变性,现有的ICL库预测方法可能缺乏精度.
- 多中心大数据为开发更强大的预测模型提供了机会.
研究的目的:
- 开发和验证用于预测ICL库存的机器学习 (ML) 模型.
- 评估一种新型数据处理方法的实用性,即数字库存信息系统 (DVIS),以提高ML模型性能.
- 将与DVIS集成的ML模型的预测和分类准确性与传统方法进行比较.
主要方法:
- 一项回顾性研究利用了五家医院6715只眼睛的手术前生物识别数据.
- 相互信息回归确定了关键的预测参数.
- 为了数据处理,开发了一个数字金库信息系统 (DVIS),并在内部和外部训练和验证了ML模型 (包括XGBoost).
主要成果:
- 与DVIS集成的XGBoost模型表现出优异的ICL密室预测准确度,达到39.15μm (内部) 和149.72μm (外部) 的低平均绝对误差 (MAE).
- 该模型在内部验证中获得了0.86的R2值.
- 对于ICL库存分类,XGBoost与DVIS的准确率达到81.4% (内部) 和57.27% (外部),显著超过传统的ML算法.
结论:
- DVIS提供了一种有效的数据处理策略,显著提高了ML模型的效率和准确性,用于ICL库预测.
- 这种协同方法增强了现有的ML方法,为ICL植入提供了有价值的工具,用于在ICL植入时进行知情的临床决策.
- 开发的模型显示了优化手术规划和折射手术患者的结果的前景.
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