两种机器学习模型以节省眼查程序:基于神经网络的方法
Wolfgang Hitzl1,2, Markus Lenzhofer1,2, Melchior Hohensinn1,2
1Department of Ophthalmology and Optometry, Paracelsus Medical University, University Hospital Salzburg, Muellner Hauptstrasse 48, 5020 Salzburg, Austria.
Journal of personalized medicine
|August 27, 2025
概括
这项研究开发了神经网络模型,以确定可以安全地跳过多达10年的青光眼查的患者. 模型准确预测了100%的病例,排除了43%的个人从常规查中.
科学领域:
- 眼科 眼科
- 人工智能
- 医疗诊断
背景情况:
- 眼查程序通常会发现许多不需要干预的患者.
- 很大一部分患者在10年内没有接受开角青光眼 (OAG) 或眼内压力降低疗法.
- 需要在初次检查时确定可以安全地排除在常规查之外的患者.
研究的目的:
- 开发和验证预测模型,以识别没有OAG且不需要降血压治疗的个体.
- 根据初步检查数据,评估安全地排除部分患者从青光眼查计划中.
主要方法:
- 在接受眼科检查的6889名受试者的数据上训练和测试了两个神经网络模型.
- 在模型中包含一个拒绝选项来处理不确定的预测.
- 使用客观的视神经头部和定量磁盘测量.
主要成果:
- 第一个模型对100%的病例 (253/253) 进行了正确的预测,没有OAG和IOP降压治疗,预测在57%的病例中被拒绝.
- 第二个模型对100%的病例 (271/271) 进行了正确的预测,预测在46.4%的病例中被拒绝.
- 两种模型都没有做出错误的负或错误的正预测.
结论:
- 大约43%的眼睛可以在长达10年的时间内被安全地排除在查之外.
- 开发的模型大大减少了眼科医生的查工作量.
- 预测模型的未来进步可以进一步提高玻璃眼查的效率和成本效益.
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