June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Comparative Assessment of OCT Segmentation Metrics between Manual Corrected Image Processing and Machine Learning in Severe Inflammatory Disease and Retinal Dystrophy
Author Affiliations & Notes
  • Megan Ann McDonald
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Cindy Chen
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Michael Ramos
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Jon Whitney
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Kimberly Baynes
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Peter M Kaiser
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Danielle Burton
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Sumit Sharma
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Justis P Ehlers
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Sunil K. Srivastava
    Cleveland Clinic Cole Eye Institute, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Megan McDonald, None; Cindy Chen, None; Michael Ramos, None; Jon Whitney, None; Kimberly Baynes, None; Peter Kaiser, None; Danielle Burton, None; Sumit Sharma, Alimera (C), Allergan (C), Bausch and Lomb (C), Clearside (C), Eyepoint (C), Genentech (C), Regeneron (C); Justis Ehlers, Aerpio (C), Alcon (C), Allegro (C), Allergan (C), Genentech (C), Leica (C), Novartis (C), Oxurion (C), Regeneron (C), Roche (C), Santen (C), Zeiss (C); Sunil K. Srivastava, Abbvie (C), Allergan (F), Bausch and Lomb (C), Eyepoint (F), Eyepoint (C), Eyevensys (C), Novartis (C), Regeneron (F), Regeneron (C), Santen (F), Zeiss (C)
  • Footnotes
    Support  NIH-NEI P30 Core Grant (IP30EY025585), Unrestricted Grants from The Research to Prevent Blindness, Inc., Cleveland Eye Bank Foundation
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2454. doi:
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      Megan Ann McDonald, Cindy Chen, Michael Ramos, Jon Whitney, Kimberly Baynes, Peter M Kaiser, Danielle Burton, Sumit Sharma, Justis P Ehlers, Sunil K. Srivastava; Comparative Assessment of OCT Segmentation Metrics between Manual Corrected Image Processing and Machine Learning in Severe Inflammatory Disease and Retinal Dystrophy. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2454.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To determine the differences in optical coherence tomography (OCT) segmentation metrics between machine learning based assessment and an expert-reader corrected logic based algorithm in eyes with severe outer retinal diseases. A machine learning based segmentation model created from normal, diabetic retinopathy and age related macular degeneration eyes was assessed in this experiment. OCT images from severe outer retinal diseased eyes (uveitis patients with syphilitic retinitis and retinitis pigmentosa) were analyzed and compared to a manually corrected expert readers.

Methods : 21 eyes were included in this experiment (9 RP and 12 syphilitic). For this analysis, the inner limiting membrane (ILM), outer nuclear layer (ONL), ellipsoid zone (EZ), and retinal pigment epithelium (RPE) were segmented using both deep learning and a logic based algorithm (LBA) with expert reader (ER)-corrected segmentation. The intraclass correlation coefficients (ICC) for ER-corrected LBA and machine learning were calculated and compared using segmentation metrics for central subfield thickness and central subfield volume. Intraclass correlation coefficients were created (MS-Excel, RealStatistics 2020) with 95% confidence intervals.

Results : ICC scores were highest for total retinal thickness measurements including central subfield thickness (.619) and central foveal mean thickness (.826). Machine learning displayed poor correlation when compared to ER-corrected LBA for all EZ based measures including EZ/RPE central subfield thickness (ICC 0.062) and central subfield volume (ICC 0.059). Enface based measures including maps detailing zero micron thickness and less than 20 micron thickness also had poor correlations. When separating out by disease subtype, there were no differences between correlations in syphilis retinopathy nor in RP.

Conclusions : In this machine learning model created from normal, AMD and DR pathologies, there was poor correlation to expert readers when assessing EZ based metrics in eyes with severe outer retinal pathology. Though our numbers are small, specific models created from manually graded severe retinal pathology may be needed to improve performance when assessing these complicated eyes. When assessing severe EZ pathology, our results suggest that manual grading is still needed.

This is a 2021 ARVO Annual Meeting abstract.

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