Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases.

Journal: JAMA ophthalmology
Published Date: Oct 1, 2020

Abstract

IMPORTANCE: Recent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, criterion standard-annotated retinal data sets for training. Low-shot learning algorithms, aiming to learn from a relatively low number of training data, may be beneficial for clinical situations involving rare retinal diseases or when addressing potential bias resulting from data that may not adequately represent certain groups for training, such as individuals older than 85 years.

Authors

  • Philippe Burlina
    Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
  • William Paul
    Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland.
  • Philip Mathew
    Applied Physics Laboratory, The Johns Hopkins University, Baltimore, Maryland.
  • Neil Joshi
    Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland, United States of America.
  • Katia D Pacheco
    Retina Division, Brazilian Center of Vision Eye Hospital, DF, Brazil.
  • Neil M Bressler
    Wilmer Eye Institute, Johns Hopkins University, Baltimore, Maryland4Editor, JAMA Ophthalmology.

Keywords

External Resources

Popular Topics
Recent Journals