开发一种机器学习模型,以预测低视力辅助器适合视力障碍患者
Bingfa Dai1,2, Pengpeng Pei3, Zunqi Kan4
1Department of Ophthalmology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Frontiers in medicine
|January 28, 2026
概括
使用机器学习的AI模型可以帮助安装低视力辅助器 (LVA),提高可访问性. 随机森林模型准确地预测了远距离光学,近电子和近光学视觉辅助器件的处方.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 目前,安装低视力辅助器 (LVA) 需要专门的专业知识,限制了全球访问.
- 一个基于人工智能 (AI) 的模型被开发出来,用于自动化和协助LVA安装.
研究的目的:
- 开发和验证用于预测LVA处方的机器学习模型.
- 为了比较随机森林 (RF),深度神经网络 (DNN) 和后勤回归 (LR) 模型的性能.
- 确定影响LVA选择的关键临床因素.
主要方法:
- 收集了来自中国东南部1,241名视力低下患者 (2015-2021) 的临床数据.
- 训练并测试了RF,DNN和LR模型,以预测远光视觉辅助器 (DOV),近电子视觉辅助器 (NEV) 和近光视觉辅助器 (NOV) 的处方.
- 在外部数据上验证了最佳模型,并将其性能与经验丰富的眼科医生进行了比较.
主要成果:
- 射频模型表现出优异的性能,AUC值为0.93 (DOV),0.83 (NEV) 和0.91 (NOV).
- 患者年龄,最佳校正视敏度 (BCVA) 和咨询年份是所有LVA类别的关键预测因素.
- 外部验证显示,人工智能模型的性能与职业生涯中期的眼科医生相当.
结论:
- 机器学习模型可以根据临床数据有效预测LVA处方.
- 开发的AI工具提供了基于数据的建议,有可能改善LVA配件的可访问性.
- 确定了患者特征和LVA处方模式之间的显著关联.
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