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|Title:||Comparison of Anterior Segment Optical Tomography Parameters Measured Using a Semi-Automatic Software to Standard Clinical Instruments||Authors:||Ang, M.
|Issue Date:||4-Jun-2013||Citation:||Ang, M., Chong, W., Huang, H., Tay, W.T., Wong, T.Y., He, M.-G., Aung, T., Mehta, J.S. (2013-06-04). Comparison of Anterior Segment Optical Tomography Parameters Measured Using a Semi-Automatic Software to Standard Clinical Instruments. PLoS ONE 8 (6) : -. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0065559||Abstract:||Objective:To compare anterior segment parameters measured using a semi-automatic software (Zhongshan Angle Assessment Program, ZAP) applied to anterior segment optical coherence tomography (AS-OCT) images, with commonly used instruments.Methods:Cross-sectional study of a total of 1069 subjects (1069 eyes) from three population-based studies of adults aged 40-80 years. All subjects underwent AS-OCT imaging and ZAP software was applied to determine anterior chamber depth (ACD), central corneal thickness (CCT), anterior and keratometry (K) - readings. These were compared to auto-refraction, keratometry and ocular biometry measured using an IOLMaster, ultrasound pachymeter and auto-refractor respectively. Agreements between AS-OCT (ZAP) and clinical instrument modalities were described using Bland-Altman, 95% limits of agreement (LOA).Results:The mean age of our subjects was 56.9±9.5 years and 50.9% were male. The mean AS-OCT (ZAP) parameters of our study cohort were: ACD 3.29±0.35 mm, CCT 560.75±35.07 μm; K-reading 46.79±2.72 D. There was good agreement between the measurements from ZAP analysis and each instrument and no violations in the assumptions of the LOA; albeit with a systematic bias for each comparison: AS-OCT consistently measured a deeper ACD compared to IOLMaster (95% LOA -0.24, 0.55); and a thicker CCT for the AS-OCT compared to ultrasound pachymetry (16.8±0.53 μm 95% LOA -17.3, 50.8). AS-OCT had good agreement with auto-refractor with at least 95% of the measurements within the prediction interval (P value||Source Title:||PLoS ONE||URI:||http://scholarbank.nus.edu.sg/handle/10635/108307||ISSN:||19326203||DOI:||10.1371/journal.pone.0065559|
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