[Skip to Navigation]
Invited Commentary
December 30, 2021

Machine Learning–Based Anomaly Detection Techniques in Ophthalmology

Author Affiliations
  • 1Department of Ophthalmology, University of Washington School of Medicine, Seattle
  • 2Karalis Johnson Retina Center, Seattle, Washington
JAMA Ophthalmol. 2022;140(2):189-190. doi:10.1001/jamaophthalmol.2021.5555

As advances in deep learning have quickly permeated into the field of ophthalmology, supervised deep learning approaches have achieved great performance in eye disease classification tasks such as diabetic retinopathy, age-related macular degeneration, and glaucoma.1-3 However, disease-specific classification models are limited in scope with regard to detecting other ocular diseases that are not included in the training set. One solution to this is to develop a multiclass model trained on a broad array of ocular diseases. However, this solution would require large data sets comprising comprehensive and balanced distributions of many ocular diseases, as well as necessity for labor- and cost-intensive sample annotations. A more scalable solution would be to use a semisupervised or unsupervised approach in order to train a general anomaly detection algorithm. In addition to reducing the cost of image annotation, AD models could also screen large populations ahead of a more comprehensive workup.

Add or change institution