Departamento de Ciencias Politécnicas, Universidad Católica de Murcia UCAM, 30107 Guadalupe, Spain.
Departamento de Tecnologías de la Información y Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
Sensors (Basel). 2022 Jun 27;22(13):4842. doi: 10.3390/s22134842.
: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. : Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. : Machine learning models were designed and optimized, specifically decision trees, based on the values of proposed asymmetry metrics. The use of these models on the dataset provided good classification of the patients (accuracy 88%, sensitivity 70%, specificity 93% and precision 75%). : The obtained machine learning models based on retinal nerve fiber layer asymmetry are simple but effective methods which offer a good trade-off in classification of patients and simplicity. The fast binary classification relies on a few asymmetry values of the retinal nerve fiber layer thickness, allowing their use in the daily clinical practice for glaucoma screening.
: 本研究旨在分析使用光学相干断层扫描进行青光眼筛查时同一患者双眼不对称特征的相关性。: 研究中使用频域光学相干断层扫描仪来估计患者双眼的视盘周围视网膜神经纤维层厚度。这些测量值是从健康和青光眼患者的数据集收集的。然后提出了几种用于评估双眼视网膜神经纤维层厚度不对称的指标。通过对数据集进行统计分析来评估这些指标的重要性，作为青光眼诊断的相关特征。最后，通过设计可用于青光眼早期诊断的监督机器学习模型来证明这些不对称特征的有用性。: 设计并优化了机器学习模型，特别是决策树，基于提出的不对称指标的值。在数据集上使用这些模型可以很好地对患者进行分类（准确率 88%，灵敏度 70%，特异性 93%和精度 75%）。: 基于视网膜神经纤维层不对称的获得的机器学习模型是简单但有效的方法，在患者分类和简单性方面提供了很好的折衷。快速的二进制分类依赖于几个视网膜神经纤维层厚度的不对称值，允许它们在日常临床实践中用于青光眼筛查。
