开发一个神经网络来预测最佳的IOP减少在眼管理中的神经网络
Raheem Remtulla1, Sidrat Rahman2, Hady Saheb1,2,3
1McGill Academic Eye Centre, McGill University, Montreal, QC H4A 3J1, Canada.
Vision (Basel, Switzerland)
|October 24, 2025
概括
神经网络模型可以预测眼患者的个性化眼内压力 (IOP) 降低目标,包括正常张力眼 (NTG). 这支持个性化治疗和更好的患者结果.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 玻璃眼的治疗需要降低眼内压力 (IOP),但很难确定精确的目标降低,特别是在正常张力玻璃眼 (NTG).
- 个性化治疗策略对于优化青光眼护理和预防疾病进展至关重要.
研究的目的:
- 开发和验证一种神经网络模型,用于预测青光眼患者个性化的IOP降低目标.
- 评估模型的性能,并确定个性化的玻璃眼管理的关键预测特征.
主要方法:
- 通过对270名患者 (118名NTG患者) 的回顾性临床数据进行单层人工神经网络的训练.
- 输入参数包括人口,折射,结构和功能数据;输出是IOP减少.
- 数据被分为培训 (65%),验证 (15%) 和测试 (20%) 集,模型被训练了10次.
主要成果:
- 神经网络在训练,验证和测试数据集中表现强且一致 (平均RMSE约2.0-2.2,皮尔森r约0.88-0.92).
- 最好的模型实现了1.57-2.90的RMSE和0.91-0.93.90的r值.
- 特征除发现了IOP,轴长,中角膜厚度 (CCT),诊断,视野指数,球体等价值,平均偏差和横向性的显著贡献.
结论:
- 一个简单的神经网络可以可靠地预测个人化IOP减少目标,以治理青光眼.
- 这种预测能力支持个性化治疗策略,可能导致改善患者在绿眼的治疗结果.
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