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Variability in Plus Disease Diagnosis using Single and Serial Images.

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Retinopathy of prematurity (ROP) diagnosis varied among graders using single versus serial retinal images. Deep learning shows potential for standardizing ROP assessment and treatment.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants.
  • Accurate diagnosis and grading of ROP are critical for timely intervention.
  • Current diagnostic methods rely on expert interpretation of retinal images, which can be subjective.

Purpose of the Study:

  • To evaluate how the use of serial retinal images impacts the diagnosis of retinopathy of prematurity (ROP) compared to single images.
  • To assess the variability in ROP grading among clinicians when using single versus serial imaging data.
  • To explore the utility of a deep learning system in quantifying ROP severity.

Main Methods:

  • A cohort study involving 15 ROP cases from the i-ROP consortium.
  • Seven ophthalmologists graded single and serial retinal images for ROP severity (plus, preplus, none).
  • A deep learning system (i-ROP) generated a vascular severity score (VSS) for each image.

Main Results:

  • Over 50% of graders showed changes in ROP severity grading when using serial images, with significant inter-grader variability (Cohen's kappa 0.29-1.0).
  • Serial imaging particularly influenced grading for preplus disease.
  • The ROP VSS correlated well with expert classifications of plus disease and demonstrated agreement with disease progression.

Conclusions:

  • Clinician variability in ROP diagnosis exists, influenced by the use of single versus serial retinal images.
  • Deep learning-based quantitative assessment offers a promising approach to standardize ROP diagnosis and treatment.