Diagnosis of glaucoma and detection of glaucoma progression using spectral domain optical coherence tomography.
Journal Article (Journal Article;Review)
PURPOSE OF REVIEW: With the rapid adoption of spectral domain optical coherence tomography (SDOCT) in clinical practice and the recent advances in software technology, there is a need for a review of the literature on glaucoma detection and progression analysis algorithms designed for the commercially available instruments. RECENT FINDINGS: Peripapillary retinal nerve fiber layer (RNFL) thickness and macular thickness, including segmental macular thickness calculation algorithms, have been demonstrated to be repeatable and reproducible, and have a high degree of diagnostic sensitivity and specificity in discriminating between healthy and glaucomatous eyes across the glaucoma continuum. Newer software capabilities such as glaucoma progression detection algorithms provide an objective analysis of longitudinally obtained structural data that enhances our ability to detect glaucomatous progression. RNFL measurements obtained with SDOCT appear more sensitive than time domain OCT (TDOCT) for glaucoma progression detection; however, agreement with the assessments of visual field progression is poor. SUMMARY: Over the last few years, several studies have been performed to assess the diagnostic performance of SDOCT structural imaging and its validity in assessing glaucoma progression. Most evidence suggests that SDOCT performs similarly to TDOCT for glaucoma diagnosis; however, SDOCT may be superior for the detection of early stage disease. With respect to progression detection, SDOCT represents an important technological advance because of its improved resolution and repeatability. Advancements in RNFL thickness quantification, segmental macular thickness calculation and progression detection algorithms, when used correctly, may help to improve our ability to diagnose and manage glaucoma.
- Grewal, DS; Tanna, AP
- March 2013
Volume / Issue
- 24 / 2
Start / End Page
- 150 - 161
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)
- United States