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Artificial intelligence for pediatric ophthalmology.

Julia E Reid1,2, Eric Eaton3

  • 1Nemours/Alfred I. duPont Hospital for Children, Division of Pediatric Ophthalmology, Wilmington, Delaware, USA.

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Artificial intelligence (AI) shows promise in pediatric ophthalmology, particularly for diagnosing retinopathy of prematurity. Further research and open-access data are needed to fully realize AI's potential in improving children's eye care.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Pediatric Medicine

Background:

  • Limited progress in applying AI to pediatric ophthalmology compared to general ophthalmology.
  • Pediatric eye care presents unique challenges and opportunities for AI solutions.

Purpose of the Study:

  • Discuss unique needs of pediatric patients.
  • Explore AI applications in pediatric ophthalmology.
  • Identify future research directions.

Main Methods:

  • Review of recent AI applications in pediatric ophthalmology.
  • Analysis of machine learning techniques used in the field.

Main Results:

  • Automated detection of retinopathy of prematurity rivals expert performance.
  • AI applied to pediatric cataract classification, surgical complication prediction, strabismus detection, myopia prediction, and reading disability diagnosis.
  • Machine learning used for visual development studies, vessel segmentation, and image synthesis.

Conclusions:

  • AI can enhance clinical care, access, discovery, and efficiency in pediatric ophthalmology.
  • Clinical trials demonstrating physician-level performance are necessary before patient deployment.
  • Poor reproducibility due to closed-access data hinders direct comparison; open-access data is crucial for progress.