Mardin Christian Y, Peters Andrea, Horn Folkert, Jünemann Anselm G, Lausen Berthold
Department of Ophthalmology and Eye Hospital, Friedrich-Alexander-University Erlangen-Nürnberg, D-91054 Erlangen, Schwabachanlage 6, Germany.
J Glaucoma. 2006 Aug;15(4):299-305. doi: 10.1097/01.ijg.0000212232.03664.ee.
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.
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, 24 h-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/PERI1/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.
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.
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.
本研究旨在确定仅基于测量结果，视神经乳头形态学数据与视野（VF）数据的组合是否能改善青光眼的诊断。
从埃尔朗根青光眼登记处选取88例视野检查确诊的青光眼患者和88例正常视盘，按年龄匹配。所有正常人和患者均接受标准化检查（裂隙灯生物显微镜检查、前房角镜检查、24小时压平眼压测量、自动视野检测、15度视盘立体照相以及使用海德堡视网膜断层扫描仪（HRT）对视盘进行扫描）。HRT变量在视盘的4个扇区中计算得出。所有变量均使用软件的标准参考平面进行计算。为了获得与HRT软件提供的相同扇区分组，将视野反应在4个扇区内进行平均。将这些视野反应的分类结果与4个扇区内的汇总结果进行比较。使用形态学和视野数据的6种不同组合来评估它们对疾病诊断的适用性。HRT测量结果、Octopus的标准输出（HRT/PERI1）、HRT测量结果以及汇总扇区及其标准差（HRT/PERI2）、HRT测量结果、Octopus的标准输出以及汇总扇区及其标准差（HRT/PERI1/PERI2）、Octopus的标准输出（PERI1）、Octopus的汇总扇区及其标准差（PERI2）以及HRT测量结果。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、装袋法和双重装袋法。
形态学和视野数据的组合改善了自动分类规则。对于同时使用两种诊断工具的双重装袋法，仅通过视野和HRT指标诊断青光眼的准确性最高。通过双重装袋法将HRT和视野扇区相结合，原发性开角型青光眼患者的估计误分类概率可低于0.07。因此，双重装袋法以及HRT、PERI1和PERI2的组合实现了最高敏感性95%和特异性91%。
视盘测量和视野数据的组合不仅可以在未来改善青光眼的诊断，还可以帮助找到一种客观的方法来诊断青光眼性视神经萎缩。结构与功能之间地形关系的局限性在于视盘形态的个体变异性和视野检测的主观变异性。
