June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Improving OCT Reproducibility with an Automated Corneal Scan Quality Report
Author Affiliations & Notes
  • Esther Young
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Homayoun Bagherinia
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Patricia Sha
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Michael H. Chen
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Mary K Durbin
    R&D, Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Footnotes
    Commercial Relationships   Esther Young, Carl Zeiss Meditec, Inc. (E); Homayoun Bagherinia, Carl Zeiss Meditec, Inc. (E); Patricia Sha, Carl Zeiss Meditec, Inc. (C); Michael Chen, Carl Zeiss Meditec, Inc. (C); Mary Durbin, Carl Zeiss Meditec, Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3508. doi:
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    • Get Citation

      Esther Young, Homayoun Bagherinia, Patricia Sha, Michael H. Chen, Mary K Durbin; Improving OCT Reproducibility with an Automated Corneal Scan Quality Report. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3508.

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

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Abstract

Purpose : The quality of OCT corneal scans affects clinical interpretation and the performance of algorithms that generate corneal maps and other measurements. A scan quality report would help operators of all skill levels quickly determine whether a scan should be repeated.

Methods : The study was performed on data from a prospective single-site study where three Pachymetry scans on each of three CIRRUS™ HD-OCT 5000 (ZEISS, Dublin, CA) devices were acquired on subjects in three groups: Normal Cornea (n=45), Post-LASIK (n=40), and Corneal Pathology (n=37).

After each scan, a quality metric algorithm automatically evaluates the scan and displays an acceptability report on the screen. All scans were saved, but in post-analysis, scans were excluded where the quality report indicated the scan was not acceptable for reasons such as poor scan quality, scan too high/low, vertex off center, or large motion. Poor quality may be caused by blinks, partial blinks, eyelid/eyelash interference, or low contrast. The algorithm uses the dewarped anterior surface segmentation and its confidence value at each segmentation point to detect issues outside an acceptable range. To assess the impact of the algorithm recommendations, the reproducibility of epithelial mapping of all scans was compared with a subset of non-excluded scans.

Results : Table 1 reports the number of total number of scans in each group (Normal, Post-LASIK, and Corneal Pathology), the number of scans in each group recommended for exclusion based on the scan quality algorithm, and the reasons for exclusion. Table 2 demonstrates improvement in reproducibility of epithelial mapping once the scans deemed unacceptable by the algorithm are removed from analysis. The improvement is most significant in corneal pathology subjects and in the inferior subfield for normal corneas.

Conclusions : An automated scan quality report is helpful in improving reproducibility. It could be used to immediately alert the operator to repeat poor scans that would affect clinical interpretation or algorithm performance. Currently, when doctors receive poor scans for review, they must request for the patient to be scanned again, which is inconvenient, time-consuming, and disruptive to all parties. With this aid, workflow efficiency may be improved by increasing the consistent quality of scans by independent operators of all skill levels and decreasing the need to bring patients back for repeat scanning.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

 

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