advertisement
Abstract #8391 Published in IGR 5-1
Glaucoma detection using scanning laser polarimetry with variable corneal polarization compensation
Weinreb RN; Bowd C; Zangwill LMArchives of Ophthalmology 2003; 121: 218-224
OBJECTIVE: To compare the ability of scanning laser polarimetry (SLP) to discriminate between healthy and glaucomatous eyes with manufacturer-assumed fixed and subject-specific variable corneal polarization magnitude (CPM) and corneal polarization axis (CPA) values. METHODS: An SLP was modified to enable the measurement of CPM and CPA values so that compensation for corneal birefringence could be corrected on a subject-specific variable basis. The authors examined 40 healthy eyes and 54 glaucomatous eyes with repeatable visual field damage (average ± SD mean deviation, -6.5 ± 4.9 dB) were examined by SLP using the manufacturer-assumed fixed corneal compensation (FCC-SLP) values and subject-specific variable corneal compensation (VCC-SLP) values. Areas under the receiver operating characteristic (ROC) curve for discriminating between healthy and glaucomatous eyes using FCC-SLP and VCC-SLP parameters were compared. RESULTS: The areas under the ROC curve increased with VCC-SLP compared with FCC-SLP, particularly for all thickness parameters. The parameters with which the area under the ROC curve improved significantly from FCC-SLP to VCC-SLP included average thickness (ROC curve area, 0.62 versus 0.75), superior integral (0.66 versus 0.79), ellipse average (0.65 versus 0.80), inferior average (0.66 versus 0.80), and superior average (0.68 versus 0.83). CONCLUSION: Variable corneal compensation to correct for subject-specific CPM and CPA can improve the ability of SLP to discriminate between healthy and glaucomatous eyes.
Dr. R.N. Weinreb, Hamilton Glaucoma Center, University of California-San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0946, USA
Classification:
2.13 Retina and retinal nerve fibre layer (Part of: 2 Anatomical structures in glaucoma)
6.9.1 Laser scanning (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
