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首页> 外文期刊>Acta ophthalmologica Scandinavica >Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT
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Machine learning classifiers for glaucoma diagnosis based on classification of retinal nerve fibre layer thickness parameters measured by Stratus OCT

机译：基于Stratus OCT测量的视网膜神经纤维层厚度参数分类的青光眼诊断机器学习分类器

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Purpose: To compare the performance of two machine learning classifiers (MLCs), artificial neural networks (ANNs) and support vector machines (SVMs), with input based on retinal nerve fibre layer thickness (RNFLT) measurements by optical coherence tomography (OCT), on the diagnosis of glaucoma, and to assess the effects of different input parameters. Methods: We analysed Stratus OCT data from 90 healthy persons and 62 glaucoma patients. Performance of MLCs was compared using conventional OCT RNFLT parameters plus novel parameters such as minimum RNFLT values, 10th and 90th percentiles of measured RNFLT, and transformations of A-scan measurements. For each input parameter and MLC, the area under the receiver operating characteristic curve (AROC) was calculated. Results: There were no statistically significant differences between ANNs and SVMs. The best AROCs for both ANN (0.982, 95%CI: 0.966-0.999) and SVM (0.989, 95% CI: 0.979-1.0) were based on input of transformed A-scan measurements. Our SVM trained on this input performed better than ANNs or SVMs trained on any of the single RNFLT parameters (p < 0.038). The performance of ANNs and SVMs trained on minimum thickness values and the 10th and 90th percentiles were at least as good as ANNs and SVMs with input based on the conventional RNFLT parameters.Conclusion: No differences between ANN and SVM were observed in this study. Both MLCs performed very well, with similar diagnostic performance. Input parameters have a larger impact on diagnostic performance than the type of machine classifier. Our results suggest that parameters based on transformed A-scan thickness measurements of the RNFL processed by machine classifiers can improve OCT-based glaucoma diagnosis.
机译：目的：为了比较两种机器学习分类器（MLC），人工神经网络（ANN）和支持向量机（SVM）的性能，以及基于视网膜神经纤维层厚度（RNFLT）的光学相干断层扫描（OCT）测量输入，对青光眼的诊断，并评估不同输入参数的效果。方法：我们分析了90例健康人和62例青光眼患者的Stratus OCT数据。使用常规OCT RNFLT参数加上新参数（例如最小RNFLT值，所测量RNFLT的第10和90个百分位数以及A扫描测量的转换）来比较MLC的性能。对于每个输入参数和MLC，计算了接收器工作特性曲线（AROC）下的面积。结果：人工神经网络和支持向量机之间无统计学差异。 ANN（0.982，95％CI：0.966-0.999）和SVM（0.989，95％CI：0.979-1.0）的最佳AROC均基于转换后的A扫描测量结果。我们在此输入下训练的SVM优于在任何单个RNFLT参数上训练的ANN或SVM（p <0.038）。在最小厚度值和第10和第90个百分位数上训练的ANN和SVM的性能至少与基于常规RNFLT参数进行输入的ANN和SVM一样。结论：在本研究中未观察到ANN和SVM之间的差异。两种MLC的表现都非常好，诊断性能相似。输入参数对诊断性能的影响大于机器分类器的类型。我们的结果表明，基于机器分类器处理的RNFL的变换A扫描厚度测量值的参数可以改善基于OCT的青光眼的诊断。

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