Artificial Intelligence for Pediatric Ophthalmology

Julia E. Reid; Eric Eaton


Curr Opin Ophthalmol. 2019;30(5):337-346. 

In This Article

Abstract and Introduction


Purpose of review: Despite the impressive results of recent artificial intelligence applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric patients and how artificial intelligence techniques can address these challenges, surveys recent applications to pediatric ophthalmology, and discusses future directions.

Recent findings: The most significant advances involve the automated detection of retinopathy of prematurity, yielding results that rival experts. Machine learning has also been applied to the classification of pediatric cataracts, prediction of postoperative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability. In addition, machine learning techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis.

Summary: Artificial intelligence applications could significantly benefit clinical care by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Owing to the widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software could alleviate these issues and encourage further applications to pediatric ophthalmology.


The increased availability of ophthalmic data, coupled with advances in artificial intelligence and machine learning, offer the potential to positively transform clinical practice. Recent applications of machine learning techniques to general ophthalmology have demonstrated the potential for automated disease diagnosis,[1] automated prescreening of primary care patients for specialist referral,[2] and scientific discovery,[3] among others. Acting as a complement to ophthalmologists, these and future applications have the potential to optimize patient care, reduce costs and barriers to access, limit unnecessary referrals, permit objective monitoring, and enable early disease detection.

To date, most artificial intelligence applications have focused on adult ophthalmic diseases, as discussed by several reviews.[4–11] Comparatively little progress has been made in applying artificial intelligence and machine learning techniques to pediatric ophthalmology, despite the pressing need. In the United States, there is a shortage of pediatric ophthalmologists[12] and fellowship positions continue to go unfilled.[13] Globally, this shortage is even more pronounced and devastating—for example, retinopathy of prematurity (ROP), now in its third epidemic, has resulted in irreversible blindness in over 50 000 premature infants because of worldwide shortages of trained specialists and other barriers to adequate care.[14,15]