Improving glaucoma diagnosis by the combination of perimetry and HRT measurements
Improving glaucoma diagnosis by the combination of perimetry and HRT measurements
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DOI：
10.1097/01.ijg.0000212232.03664.ee
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发表时间：
2006-08-01
影响因子：
2
通讯作者：
Lausen, Berthold
中科院分区：
文献类型：
--
作者：
Mardin, Christian Y.;Peters, Andrea;Lausen, Berthold
目的：本研究的目的是确定视神经乳头形态学数据和视野 (VF) 数据的组合是否能够在单独测量的基础上改善青光眼的诊断。 患者和方法：将来自埃尔兰根青光眼登记处的 88 个视野青光眼和 88 个正常视盘进行年龄匹配。所有正常人和患者均以标准化方式进行检查（裂隙灯生物显微镜检查、房角镜检查、24 小时压平眼压测量、自动 VF 测试、15 度视盘立体摄影和海德堡视网膜断层扫描 (HRT) 视盘扫描）。 HRT 变量是在 4 个视神经盘扇区中计算的。所有变量均使用软件的标准参考平面计算。为了获得与 HRT 软件提供的相同的扇区分配，VF 响应在 4 个扇区内进行平均。这些 VF 反应的分类结果与 4 个部门内的汇总结果进行了比较。使用形态学和 VF 数据的六种不同组合来评估其诊断疾病的适用性。 HRT 测量值和八达通的标准输出 (HRT/PERI1)、HRT 测量值和汇总扇区及其标准差 (HRT/PERI2)、HRT 测量值、章鱼的标准输出和汇总扇区及其标准差 (HRT/PERII/PERI2)、八达通的标准输出 (PERI1)、八达通的汇总扇区及其标准差 (PERI2) 和 HRT 测量值。为了评估不同数据集的诊断价值，应用了机器学习分类器、稳定线性判别分析、分类树、装袋和双装袋。结果：形态学和 VF 数据的结合改进了自动分类规则。对于使用这两种诊断工具的双袋装，仅通过 VF 和 HRT 指数诊断青光眼的准确性最大化。对于原发性开角型青光眼患者，通过双套袋结合 HRT 和 VF 扇区，可以实现小于 0.07 的估计错误分类概率。因此，通过双重包装以及 HRT、PERI1 和 PERI2 组合实现最高灵敏度为 95%，特异性为 91%。 结论：视盘测量和 VF 数据的结合不仅可以改善未来青光眼的诊断，而且还有助于找到诊断青光眼视神经萎缩的客观方法。结构和功能之间的地形关系的局限性是视盘形态的个体差异和VF测试的主观差异。
Purpose: The aim of this study was to determine, whether the combination of morphologic data of the,optic nerve head and visual field (VF) data would improve diagnosis of glaucoma, on the basis of the measurements alone.Patients and Methods: Eighty-eight perimetric glaucomatous and 88 normal optic discs from the Erlangen Glaucoma Registry were matched for age. All normals and patients were examined in a standardized manner (Slitlamp biomicroscopy, gonioscopy, 24h-applanation tonometry, automated VF testing, 15-degree optic disc stereographs, and Heidelberg Retina Tomograph (HRT)-scanning of the optic disc). The HRT variables were calculated in 4 optic disc sectors. All variables were calculated with the software's standard reference plane. To gain the same allocation of sectors as provided by the HRT software, the VF responses were averaged within 4 sectors. Classification results of these VF responses were compared with the summarized results within 4 sectors. Six different combinations of morphologic and VF data were used to assess their suitability to diagnose the disease. HRT measurements, and the standard output of the Octopus (HRT/PERI1), HRT measurements and the summarized sectors and their standard deviations (HRT/PERI2), HRT measurements, standard output of the octopus and the summarized sectors and their standard deviations (HRT/PERII/PERI2), standard output of the Octopus (PERI1), summarized sectors of the Octopus and their standard deviations (PERI2) and HRT measurements. To assess the diagnostic value of the different data sets machine learning classifiers, stabilized linear discriminant analysis, classification trees, bagging, and double-bagging were applied.Results: Combination of morphologic and VF data improved the automated classification rules. The accuracy to diagnose glaucoma just by VF and HRT indices was maximized for double-bagging using both diagnostic tools. An estimated misclassification probability of less than 0.07 could be achieved for the primary open angle glaucoma patients combining HRT and VF sectors by double bagging. So highest sensitivity was 95% and specificity 91%, achieved by double-bagging and combination of HRT, PERI1, and PERI2.Conclusions: The combination of optic disc measurements and VF data could not only improve glaucoma diagnosis in future, but could also help to find an objective way to diagnose glaucomatous optic atrophy. The limitation of the topographic relationship between structure and function is the individual variability of the optic disc morphology and the subjective variability of VF testing.