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.
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.
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.