Abstract
Purpose:
Despite the development and establishment of state-of-the-art retinal imaging technology, there are few good clinical studies assessing objective changes in patients with glaucoma suspect. The aim of this study was to prospectively assess, using different objective imaging parameters, the retinal anatomical features of patients with glaucoma suspect.
Methods:
60 patients referred from general practitioners with a high risk factor for glaucoma were assessed at the Western Eye Hospital in London. 60 healthy were also assessed as control. Patients were assessed with Heidelberg Retinal Tomography (HRT3), and Spectral Domain Optical Coherence Tomography (SD-OCT) as well as Humphrey Visual Field (HVF) repeatedly from the first visit up to eighteen months follow-up. OCT Thickness map, OCT posterior pole analysis, OCT RNFL thickness profile, HRT stereometric Parameters, Moorefield Regression Analysis (MRA), GPS Classification along with a custom image analysis was used to compare retinal features and optic nerve head structures. Repeated imaging was performed in all patients.
Results:
70% of patients with glaucoma suspect showed retinal abnormalities at the OCT with a superotemporal and inferotemporal thinning of the peripapillary retina beyond the OCT RNFL and HRT scanning area. 54% had abnormal RNFL thickness profile compared to healthy subjects. 60% of the patients showed abnormal MRA which was in keeping with GPS classification. All healthy subjects had no pathological findings. Custom designed image analysis showed consistency between OCT and HRT findings.
Conclusions:
This study shows that state-of-the-art imaging technology increases the sensitivity and specificity in early diagnosis for glaucoma when different imaging devices are used simultaneously. Recognisable modification in patients with glaucoma suspect occurs when both HRT and OCT are used. It confirms that early modifications in retinal structure are assessable but only when a multi-platform approach is applied.
Keywords: 550 imaging/image analysis: clinical •
549 image processing