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Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size.

Publication ,  Journal Article
Patel, TP; Prajna, NV; Farsiu, S; Valikodath, NG; Niziol, LM; Dudeja, L; Kim, KH; Woodward, MA
Published in: Cornea
March 2018

To assess variability in corneal ulcer measurements between ophthalmologists and reduce clinician-dependent variability using semiautomated segmentation of the ulcer from photographs.Three ophthalmologists measured 50 patients' eyes for epithelial defects (EDs) and the stromal infiltrate (SI) size using slit-lamp (SL) calipers. SL photographs were obtained. An algorithm was developed for semiautomatic segmenting of the ED and SI in the photographs. Semiautomatic segmentation was repeated 3 times by different users (2 ophthalmologists and 1 trainee). Clinically significant variability was assessed with intraclass correlation coefficients (ICCs) and the percentage of pairwise measurements differing by ≥0.5 mm. Semiautomatic segmentation measurements were compared with manual delineation of the image by a corneal specialist (gold standard) using Dice similarity coefficients.Ophthalmologists' reliability in measurements by SL calipers had an ICC from 0.84 to 0.88 between examiners. Measurements by semiautomatic segmentation had an ICC from 0.96 to 0.98. SL measures of ulcers by clinical versus semiautomatic segmentation measures differed by ≥0.5 mm in 24% to 38% versus 8% to 28% (ED height); 30% to 52% versus 12% to 34% (ED width); 26% to 38% versus 10% to 32% (SI height); and 38% to 58% versus 14% to 34% (SI width), respectively. Average Dice similarity coefficients between manual and repeated semiautomatic segmentation ranged from 0.83 to 0.86 for the ED and 0.78 to 0.83 for the SI.Variability exists when measuring corneal ulcers, even among ophthalmologists. Photography and computerized methods for quantifying the ulcer size could reduce variability while remaining accurate and impact quantitative measurement endpoints.

Duke Scholars

Published In

Cornea

DOI

EISSN

1536-4798

ISSN

0277-3740

Publication Date

March 2018

Volume

37

Issue

3

Start / End Page

331 / 339

Related Subject Headings

  • Slit Lamp Microscopy
  • Reproducibility of Results
  • Photography
  • Ophthalmology & Optometry
  • Observer Variation
  • Middle Aged
  • Male
  • Image Processing, Computer-Assisted
  • Humans
  • Female
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Patel, T. P., Prajna, N. V., Farsiu, S., Valikodath, N. G., Niziol, L. M., Dudeja, L., … Woodward, M. A. (2018). Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size. Cornea, 37(3), 331–339. https://doi.org/10.1097/ico.0000000000001488
Patel, Tapan P., N Venkatesh Prajna, Sina Farsiu, Nita G. Valikodath, Leslie M. Niziol, Lakshey Dudeja, Kyeong Hwan Kim, and Maria A. Woodward. “Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size.Cornea 37, no. 3 (March 2018): 331–39. https://doi.org/10.1097/ico.0000000000001488.
Patel TP, Prajna NV, Farsiu S, Valikodath NG, Niziol LM, Dudeja L, et al. Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size. Cornea. 2018 Mar;37(3):331–9.
Patel, Tapan P., et al. “Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size.Cornea, vol. 37, no. 3, Mar. 2018, pp. 331–39. Epmc, doi:10.1097/ico.0000000000001488.
Patel TP, Prajna NV, Farsiu S, Valikodath NG, Niziol LM, Dudeja L, Kim KH, Woodward MA. Novel Image-Based Analysis for Reduction of Clinician-Dependent Variability in Measurement of the Corneal Ulcer Size. Cornea. 2018 Mar;37(3):331–339.

Published In

Cornea

DOI

EISSN

1536-4798

ISSN

0277-3740

Publication Date

March 2018

Volume

37

Issue

3

Start / End Page

331 / 339

Related Subject Headings

  • Slit Lamp Microscopy
  • Reproducibility of Results
  • Photography
  • Ophthalmology & Optometry
  • Observer Variation
  • Middle Aged
  • Male
  • Image Processing, Computer-Assisted
  • Humans
  • Female