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Bryan M. Wong, Richard W. Cheng, Efrem D. Mandelcorn, Edward Margolin, Sherif El-Defrawy, Peng Yan, Anna T. Santiago, Elena Leontieva, Wendy Lou, ONDRI Investigators, Wendy Hatch, Christopher Hudson; Validation of Optical Coherence Tomography Retinal Segmentation in Neurodegenerative Disease. Trans. Vis. Sci. Tech. 2019;8(5):6. https://doi.org/10.1167/tvst.8.5.6.
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This study assessed agreement between an automated spectral-domain optical coherence tomography (SD-OCT) retinal segmentation software and manually corrected segmentation to validate its use in a prospective clinical study of neurodegenerative diseases (NDD).
The sample comprised 30 subjects with NDD, including vascular cognitive impairment, frontotemporal dementia, Parkinson's disease, and Alzheimer's disease. Macular SD-OCT scans were acquired and segmented using Heidelberg Spectralis. For the central foveal B scan of each eye, eight segmentation lines were examined to determine the proportion of each line that the software erroneously delineated. Errors in four lines were manually corrected in all B scans spanning a 6-mm circle centered on the foveola. Mean volume and thickness measurements for four retinal layers (total retina, retinal nerve fiber layer [RNFL], inner retinal layers, and outer retinal layers) were obtained before and after correction.
The outer plexiform layer line had one of the lowest mean error ratios (2%), while RNFL had the highest (23%). Agreement between automated software and trained observer was excellent (ICC > 0.98) for retinal thickness and volume of all layers. Mean volume differences between software and observers for the four layers ranged from −0.003 to 0.006 mm3. Mean thickness differences ranged from −1.855 to 1.859 μm.
Despite occasional small errors in software-generated retinal sublayer segmentation, agreement was excellent between software-derived and observer-corrected mean volume and thickness sublayer measurements.
Automated SD-OCT segmentation software generates valid measurements of retinal layer volume and thickness in NDD subjects, thereby avoiding the need to manually correct nonobvious delineation errors.
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