Purpose: 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.
Methods: 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.
Results: 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).
Conclusions: 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.