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首页> 外文期刊>Investigative ophthalmology & visual science >Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography.
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Development and comparison of automated classifiers for glaucoma diagnosis using Stratus optical coherence tomography.

机译：使用Stratus光学相干断层扫描技术开发和比较用于青光眼诊断的自动分类器。

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PURPOSE: To develop and compare the ability of several automated classifiers to differentiate between normal and glaucomatous eyes based on the quantitative assessment of summary data reports from Stratus optical coherence tomography (OCT; Carl Zeiss Meditec Inc., Dublin, CA) in a Chinese population in Taiwan. METHODS: One randomly selected eye from each of 89 patients with glaucoma and each of 100 age- and sex-matched normal individuals were included in the study. Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head analysis results) were obtained by Stratus OCT. With the Stratus OCT parameters used as input, receiver operative characteristic (ROC) curves were generated by three methods, to classify eyes as either glaucomatous or normal: linear discriminant analysis (LDA), Mahalanobis distance (MD), and artificial neural network (ANN). The area under the ROC curve was optimized by principal component analysis (PCA). Classification accuracy was determined by cross validation. RESULTS: The average visual field mean deviation was -0.7 +/- 0.6 dB in the normal group and -2.7 +/- 1.9 dB in the glaucoma group. The areas under the ROC curves were 0.824 (LDA), 0.849 (MD), 0.821 (ANN), 0.915 (LDA with PCA), 0.991 (MD with PCA), and 0.874 (ANN with PCA). CONCLUSIONS: With Stratus OCT parameters used as input, automated classifiers show promise for discriminating between glaucomatous and normal eyes. MD measured from multivariate data can predict the severity of glaucoma through the construction of a measurement space. After PCA, implementation results show that the Mahalanobis space created by MD surpasses LDA and ANN in diagnosing glaucoma.
机译：目的：基于对来自中国人口的Stratus光学相干断层扫描（OCT;卡尔·蔡司·梅迪泰克公司（Carl Zeiss Meditec Inc.），加利福尼亚州都柏林）的总结数据的定量评估，开发和比较几种自动分类器区分正常眼和青光眼的能力。在台湾方法：从89例青光眼患者中随机选出一只眼睛，并在100名年龄和性别匹配的正常个体中分别纳入研究。通过Stratus OCT测量青光眼变量（视网膜神经纤维层厚度和视神经头分析结果）。使用Stratus OCT参数作为输入，通过三种方法生成接收器操作特征（ROC）曲线，以将眼睛分类为青光眼或正常：线性判别分析（LDA），马氏距离（MD）和人工神经网络（ANN） ）。 ROC曲线下的面积通过主成分分析（PCA）进行了优化。通过交叉验证确定分类准确性。结果：正常组的平均视野平均偏差为-0.7 +/- 0.6 dB，青光眼组的平均视野平均偏差为-2.7 +/- 1.9 dB。 ROC曲线下的面积为0.824（LDA），0.849（MD），0.821（ANN），0.915（LDA与PCA），0.991（MD与PCA）和0.874（ANN与PCA）。结论：使用Stratus OCT参数作为输入，自动分类器显示了区分青光眼和正常眼睛的希望。根据多元数据测得的MD可以通过构建测量空间来预测青光眼的严重程度。在PCA之后，实施结果表明，在诊断青光眼方面，由MD创建的Mahalanobis空间超过LDA和ANN。

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