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预测眼的模式标准偏差:利用临床数据的机器学习方法

Raheem Remtulla¹, Patrik Abdelnour², Daniel R Chow²

¹Department of Ophthalmology & Visual Sciences, McGill University, Montreal, QC H4A 0A4, Canada.

Vision (Basel, Switzerland)

|September 22, 2025

概括

机器学习准确地预测了使用临床数据进行青光眼管理的模式标准偏差 (PSD). 这种自动神经网络 (ANN) 方法为传统视野 (VF) 测试提供了可靠的替代方案.

科学领域:

眼科医生眼科眼科
人工智能的人工智能
医学诊断医学诊断医学诊断

背景情况:

视野 (VF) 测试对于玻璃眼病管理至关重要,但在技术人员的可用性和测试可靠性方面面临挑战.
为改善患者护理和诊断效率,开发用于VF评估的替代方法至关重要.

研究的目的:

研究机器学习的潜力,特别是自动神经网络 (ANN),以利用现有临床数据预测模式标准偏差 (PSD).
评估ANN模型在预测PSD方面的准确性和可靠性.

主要方法:

这是一项回顾性研究,利用743只眼睛的公开数据 (541只眼睛患有青光眼,202只眼睛为对照者).
一个ANN模型使用七个输入特征进行训练:平均视网膜神经纤维层 (RNFL),眼内压 (IOP),患者年龄,中枢角膜厚度 (CCT),青光眼诊断,研究方案和侧向性.
模型性能使用根平均平方误差 (RMSE) 和相关系数 (r) 进行了评估,并对特征重要性进行了离开一个特征 (LOFO) 分析.

主要成果:

该ANN模型表现出强大的预测准确度,最小的过拟合,在训练和测试集上实现高相关系数 (r ≈ 0.81-0.89) 和低RMSE (≈ 1.67-2.27).
LOFO分析确定了RNFL,CCT,IOP,青光眼状态,研究方案和年龄是PSD预测的最重要贡献者.
该模型显示了构造有效性,成功地从RNFL和临床数据中预测PSD.

结论:

神经网络可以有效地使用RNFL和标准临床输入预测PSD,为玻璃眼评估提供了一个有前途的工具.
这种机器学习方法有可能预测或生成VF估计,可能克服当前VF测试方法的局限性.

关键词:

玻璃眼 glaucoma 玻璃眼玻璃眼玻璃眼玻璃眼机器学习是机器学习.视觉领域的视觉领域.

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