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Srinivas R. Sadda, Sandra Joeres, Ziqiang Wu, Paul Updike, Peggy Romano, Allyson T. Collins, Alexander C. Walsh; Error Correction and Quantitative Subanalysis of Optical Coherence Tomography Data Using Computer-Assisted Grading. Invest. Ophthalmol. Vis. Sci. 2007;48(2):839-848. doi: 10.1167/iovs.06-0554.
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purpose. To demonstrate feature subanalysis and error correction of optical coherence tomography (OCT) data by using computer-assisted grading.
methods. The raw exported StratusOCT (Carl Zeiss Meditec, Inc., Dublin, CA) scan data from 20 eyes of 20 patients were analyzed using custom software (termed OCTOR) designed to allow the user to define manually the retinal borders on each radial line scan. Measurements calculated by the software, including thickness of the nine standard macular subfields, foveal center point (FCP), and macular volume, were compared between two graders and with the automated Stratus analysis. Mean and range of differences for each parameter were calculated and assessed by Bland-Altman plots and Pearson correlation coefficients. Additional cases with clinically relevant subretinal findings were selected to demonstrate the capabilities of this system for quantitative feature subanalysis.
results. Retinal thickness measurements for the various subfields and the FCP showed a mean difference of 1.7 μm (maximum, 7 μm) between OCTOR graders and a mean difference of 2.3 μm (maximum of 8 μm) between the OCTOR and Stratus analysis methods. Volume measurements between Stratus and OCTOR methods differed by a mean of 0.06 mm3 (in reference to a mean macular volume of 6.81 mm3). The differences were not statistically significant, and the thicknesses correlated highly (R 2 ≥ 0.98 for all parameters).
conclusions. Manual identification of the inner and outer retinal boundaries on OCT scans can produce retinal thickness measurements consistent with those derived from the automated StratusOCT analysis. Computer-assisted OCT grading may be useful for correcting thickness measurements in cases with errors of automated retinal boundary detection and may be useful for quantitative subanalysis of clinically relevant features, such as subretinal fluid volume or pigment epithelial detachment volume.
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