Naithani Prashant, Sihota Ramanjit, Sony Parul, Dada Tanuj, Gupta Viney, Kondal Dimple, Pandey Ravindra M
Glaucoma Research Facility, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India.
Invest Ophthalmol Vis Sci. 2007 Jul;48(7):3138-45. doi: 10.1167/iovs.06-1407.
To evaluate the relationship between optic nerve head (ONH) and peripapillary retinal nerve fiber layer (RNFL) parameters by optical coherence tomography (OCT) and confocal scanning laser ophthalmoscopy (Heidelberg retinal tomography; HRT; Heidelberg Engineering, Heidelberg, Germany) in early and moderate glaucoma and to compare several OCT-based automated classifiers with those inbuilt in HRT for detection of glaucomatous damage.
This cross-sectional study included 60 eyes of 60 patients with glaucoma (30 early and 30 moderate visual field defects) and 60 eyes of 60 healthy subjects. All patients underwent Fast Optic Disc and Fast Peripapillary RNFL scans on the OCT and then HRT evaluation of the ONH during the same visit. Glaucoma variables obtained from OCT and HRT analyses were compared among the groups. Receiver operator characteristic (ROC) curves generated by performing linear discriminant analysis (LDA), artificial neural networks (ANNs), and classification and regression trees (CART) on OCT-based parameters were compared with the Moorfield regression analysis (MRA), R Bathija (RB), and FS Mickelberg (FSM) functions in the HRT, to classify eyes as either glaucomatous or normal.
No statistically significant difference was found in the disc area measured by the OCT and HRT analyses within each study group (P > 0.05). The areas under ROC curves were 0.9822 (LDF), 0.9791 (CART), and 0.9383 (ANN) as compared with 0.859 (FSM), 0.842 (RB) and 0.767 (MRA).
OCT-based automated classifiers performed better than HRT classifiers in distinguishing glaucomatous from healthy eyes. Such parameters should be integrated in the OCT to improve its diagnostic abilities.
通过光学相干断层扫描（OCT）和共焦扫描激光眼底镜检查（海德堡视网膜断层扫描；HRT；德国海德堡海德堡工程公司）评估早期和中度青光眼患者视神经乳头（ONH）和视乳头周围视网膜神经纤维层（RNFL）参数之间的关系，并比较几种基于OCT的自动分类器与HRT内置分类器在检测青光眼性损害方面的性能。
这项横断面研究纳入了60例青光眼患者的60只眼（30例早期和30例中度视野缺损）以及60名健康受试者的60只眼。所有患者在同一次就诊时接受了OCT上的快速视盘和快速视乳头周围RNFL扫描，然后进行了ONH的HRT评估。比较了从OCT和HRT分析中获得的青光眼变量在各组之间的差异。通过对基于OCT的参数进行线性判别分析（LDA）、人工神经网络（ANN）和分类与回归树（CART）生成的受试者操作特征（ROC）曲线，与HRT中的Moorfield回归分析（MRA）、R Bathija（RB）和FS Mickelberg（FSM）函数进行比较，以将眼睛分类为青光眼性或正常。
在每个研究组中，通过OCT和HRT分析测量的视盘面积没有发现统计学上的显著差异（P>0.05）。ROC曲线下面积分别为0.9822（LDF）、0.9791（CART）和0.9383（ANN），而HRT中的相应值分别为0.859（FSM）、0.842（RB）和0.767（MRA）。
基于OCT的自动分类器在区分青光眼性眼和健康眼方面比HRT分类器表现更好。这些参数应整合到OCT中以提高其诊断能力。
