Silva Fabrício R, Vidotti Vanessa G, Cremasco Fernanda, Dias Marcelo, Gomi Edson S, Costa Vital P
Glaucoma Service, Department of Ophthalmology, Universidade Estadual de Campinas, Campinas (SP), Brazil.
Arq Bras Oftalmol. 2013 May-Jun;76(3):170-4. doi: 10.1590/s0004-27492013000300008.
To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP).
Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data.
Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19).
Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.
使用光谱域光学相干断层扫描（SD - OCT）和标准自动视野计（SAP）评估机器学习分类器（MLC）对青光眼诊断的敏感性和特异性。
观察性横断面研究。纳入62例青光眼患者和48例健康个体。所有患者均接受了全面的眼科检查、消色差标准自动视野计（SAP）检查以及使用SD - OCT（Cirrus HD - OCT；卡尔蔡司医疗技术公司，加利福尼亚州都柏林）进行的视网膜神经纤维层（RNFL）成像。获得所有SD - OCT参数和SAP全局指标的受试者操作特征（ROC）曲线。随后，使用来自SD - OCT和SAP的参数测试以下MLC：装袋法（BAG）、朴素贝叶斯（NB）、多层感知器（MLP）、径向基函数（RBF）、随机森林（RAN）、集成选择（ENS）、分类树（CTREE）、Ada Boost M1（ADA）、支持向量机线性核（SVML）和支持向量机高斯核（SVMG）。将孤立的SAP和OCT参数获得的受试者操作特征曲线下面积（aROC）与使用OCT + SAP数据的MLC进行比较。
结合OCT和SAP数据，MLC的aROC范围从0.777（CTREE）到0.946（RAN）。RAN获得的最佳OCT + SAP aROC（0.946）显著大于最佳单个OCT参数的aROC（p < 0.05），但与最佳单个SAP参数获得的aROC无显著差异（p = 0.19）。
基于OCT和SAP数据训练的机器学习分类器能够成功区分健康眼睛和青光眼眼睛。与单独的OCT数据相比，OCT和SAP测量的组合提高了诊断准确性。
