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Studying disagreements among retinal experts through image analysis.

Gwénolé Quellec1, Mathieu Lamard, Béatrice Cochener

  • 1Inserm, UMR 1101, Brest, France. gwenole.quellec@inserm.fr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study introduces a new method using image analysis to find reasons for expert disagreement in diabetic retinopathy screening. It helps flag potentially contentious cases for review, improving screening accuracy.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Science

Background:

  • Diabetic Retinopathy (DR) screening relies on expert analysis of retinal images.
  • Automated image analysis aims to reduce expert workload by identifying healthy cases.
  • Inter-expert variability in DR diagnosis presents a challenge for screening programs.

Purpose of the Study:

  • To develop a novel application of image analysis algorithms for Diabetic Retinopathy screening.
  • To identify image characteristics that lead to disagreements among retinal experts.
  • To create a system that warns experts about potentially contentious examination records.

Main Methods:

  • Utilized existing image analysis algorithms for a new purpose in DR screening.
  • Developed a statistical framework employing Parzen-windowing and Patrick-Fischer distance.

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  • Analyzed disagreements among eleven experts within the Ophdiat screening program.
  • Examined an archive of 25,702 retinal examination records.

    • Main Results:

    • Successfully identified specific image characteristics contributing to expert disagreement.
    • Demonstrated the capability to flag examination records with features causing potential disputes.
    • Quantified inter-expert variability in DR screening using a novel statistical approach.

    Conclusions:

    • Image analysis algorithms can be repurposed to understand and mitigate expert disagreement in DR screening.
    • The proposed statistical framework effectively addresses the challenge of analyzing expert variability.
    • This approach has the potential to enhance the consistency and reliability of DR screening programs.