Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
Departamento de Ciencias Politécnicas, Universidad Católica de Murcia (UCAM), 30107 Guadalupe, Spain.
Sensors (Basel). 2023 May 14;23(10):4737. doi: 10.3390/s23104737.
This study aims to analyze the asymmetry between both eyes of the same patient for the early diagnosis of glaucoma. Two imaging modalities, retinal fundus images and optical coherence tomographies (OCTs), have been considered in order to compare their different capabilities for glaucoma detection. From retinal fundus images, the difference between cup/disc ratio and the width of the optic rim has been extracted. Analogously, the thickness of the retinal nerve fiber layer has been measured in spectral-domain optical coherence tomographies. These measurements have been considered as asymmetry characteristics between eyes in the modeling of decision trees and support vector machines for the classification of healthy and glaucoma patients. The main contribution of this work is indeed the use of different classification models with both imaging modalities to jointly exploit the strengths of each of these modalities for the same diagnostic purpose based on the asymmetry characteristics between the eyes of the patient. The results show that the optimized classification models provide better performance with OCT asymmetry features between both eyes (sensitivity 80.9%, specificity 88.2%, precision 66.7%, accuracy 86.5%) than with those extracted from retinographies, although a linear relationship has been found between certain asymmetry features extracted from both imaging modalities. Therefore, the resulting performance of the models based on asymmetry features proves their ability to differentiate healthy from glaucoma patients using those metrics. Models trained from fundus characteristics are a useful option as a glaucoma screening method in the healthy population, although with lower performance than those trained from the thickness of the peripapillary retinal nerve fiber layer. In both imaging modalities, the asymmetry of morphological characteristics can be used as a glaucoma indicator, as detailed in this work.
本研究旨在分析同一位患者双眼之间的不对称性，以实现青光眼的早期诊断。为了比较这两种成像方式在青光眼检测方面的不同能力，我们考虑了视网膜眼底图像和光学相干断层扫描（OCT）两种方式。从视网膜眼底图像中，我们提取了杯盘比和视盘边缘宽度之间的差异。类似地，我们在频域光学相干断层扫描中测量了视网膜神经纤维层的厚度。这些测量值被视为决策树和支持向量机模型中双眼之间的不对称特征，用于对健康和青光眼患者进行分类。本工作的主要贡献在于，使用不同的分类模型和两种成像方式，共同利用这些方式的优势，基于患者双眼之间的不对称特征，实现相同的诊断目的。结果表明，优化后的分类模型使用 OCT 双眼之间的不对称特征（敏感性 80.9%，特异性 88.2%，精确性 66.7%，准确性 86.5%）提供了更好的性能，而不是使用从视网膜图像中提取的特征，尽管我们发现了从这两种成像方式中提取的某些不对称特征之间存在线性关系。因此，基于不对称特征的模型的性能证明了它们使用这些指标区分健康人和青光眼患者的能力。基于眼底特征的模型是健康人群中进行青光眼筛查的一种有用选择，尽管性能低于基于视盘周围视网膜神经纤维层厚度的模型。在这两种成像方式中，形态特征的不对称性都可以用作青光眼的指标，正如本工作所详细说明的那样。
