Variance owing to observer, repeat imaging, and fundus camera type on cup-to-disc ratio estimates by stereo planimetry.

OBJECTIVE: To determine and compare variance components in linear cup-to-disc ratio (LCDR) estimates by computer-assisted planimetry by human experts, and automated machine algorithm (digital automated planimetry).
DESIGN: Prospective case series for evaluation of planimetry. PARTICIPANTS: Forty-four eyes of 44 consecutive patients from the outpatient Glaucoma Service at University of Iowa with diagnosis of glaucoma or glaucoma suspect were studied.
METHODS: Six stereo pairs of optic nerve photographs were taken per eye: 3 repeat stereo pairs using simultaneous fixed-stereo base fundus camera (Nidek 3Dx) and another 3 repeat stereo pairs using sequential variable-stereo base fundus camera (Zeiss). Each optic disc stereo pair was digitized and segmented into cup and rim by 3 glaucoma specialists (computer-assisted planimetry) and using a computer algorithm (digital automated planimetry), and LCDR was calculated for each segmentation (either specialist or algorithm). A linear mixed model was used to estimate mean, SD, and variance components of measurements. MAIN OUTCOME MEASURES: Average LCDR, interobserver, interrepeat, intercamera coefficients of variation (CV) of LCDR and their 95% tolerance limits.
RESULTS: There was a significant difference in LCDR estimates among the 3 glaucoma specialists. The interobserver CV of 10.65% was larger than interrepeat (6.7%) or intercamera CV (7.6%). For the algorithm, the LCDR estimate was significantly higher for simultaneous stereo fundus images (Nidek, mean: 0.66) than for sequential stereo fundus images (Zeiss, mean: 0.64), whereas interrepeat CV for Nidek (4.4%) was lower than Zeiss (6.36%); the algorithm's interrepeat and intercamera CV were 5.47% and 7.26%, respectively.
CONCLUSIONS: Interobserver variability was the largest source of variation for glaucoma specialists, whereas their interrepeat and intercamera variability is comparable with that of the algorithm. DAP reduces variability on LCDR estimates from simultaneous stereo images, such as the Nidek 3Dx.