June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Effects of Image Quality on Grading Photographs of Trachoma
Author Affiliations & Notes
  • Michelle Odonkor
    Dana Center for Preventive Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Fahd Naufal
    Dana Center for Preventive Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Beatriz Munoz
    Dana Center for Preventive Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Sheila K West
    Dana Center for Preventive Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States
  • Footnotes
    Commercial Relationships   Michelle Odonkor, None; Fahd Naufal, None; Beatriz Munoz, None; Sheila West, None
  • Footnotes
    Support  The Task Force for Global Health, Inc. P.O. #1491; grant funding from the El-Maghraby chair at the Wilmer Eye Institute (SKW)
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 2629. doi:
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    • Get Citation

      Michelle Odonkor, Fahd Naufal, Beatriz Munoz, Sheila K West; Effects of Image Quality on Grading Photographs of Trachoma. Invest. Ophthalmol. Vis. Sci. 2021;62(8):2629.

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

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Abstract

Purpose : Trachoma is the leading infectious cause of blindness worldwide. Prevalence can be assessed by field grading or grading photographs. We hypothesize that poor image quality may contribute to differential grading of trachomatous inflammation—follicular (TF) between field and photo graders.

Methods : Field grades for TF and tarsal conjunctival photographs were obtained by a field grader from one or both eyes in 3118 children from Kongwa District, Tanzania, 2 years post-MDA. 2 additional graders graded and adjudicated each photo. We identified the eyes where the field and photo TF grades did not match and assigned each eye an image quality score and a potential reason for mismatch (e.g., different interpretation of follicle number or size, field documentation error, or no obvious reason). We also assigned image quality scores to a random sample of 180 eyes with matching field and photo TF grades.

Results : 5220 eyes had matching field and photo TF grades while 177 eyes had mismatched field and photo TF grades (Fig 1). There was no difference in image quality between mismatch eyes where the field grader assigned a grade of TF and mismatch eyes where the photo graders assigned a grade of TF (p=0.6209), but there was a significant difference in image quality between eyes with matching field and photo grades and eyes with mismatched field and photo grades (p=0.0186). However, no image quality issue stood out as significantly different between eyes with matching vs. mismatched TF grades (Fig 2). Disagreement over follicle number (54.8% of mismatch eyes) or size (42.9%) were the most common potential reasons for grade mismatch.

Conclusions : Since there was a significant difference in image quality between eyes with matching vs. mismatched field and adjudicated photo grades, image quality seemed to contribute to grading disagreements between field and photo graders. Poor image quality may alter photo graders’ view of the tarsal conjunctiva and affect their determination of follicle size or number. However, as there was no difference in image quality between mismatch eyes with a field vs. photo grade of TF, image quality did not seem to influence the direction of the mismatch (i.e., which graders assigned a grade of TF).

This is a 2021 ARVO Annual Meeting abstract.

 

Fig 1. Total number of eyes with both field and photo grades.

Fig 1. Total number of eyes with both field and photo grades.

 

Fig 2. Image quality and issues for eyes where field and photo grades did or did not match. The issues are not mutually exclusive–one image could have multiple issues.

Fig 2. Image quality and issues for eyes where field and photo grades did or did not match. The issues are not mutually exclusive–one image could have multiple issues.

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