Artificial Intelligence for Pediatric Ophthalmology

TL;DR

This paper reviews recent advances in AI applications for pediatric ophthalmology, highlighting successes in disease detection, classification, and prediction, while discussing challenges like reproducibility and future directions for clinical integration.

Contribution

It surveys recent AI techniques applied to pediatric eye conditions, emphasizing the need for open data and software to improve reproducibility and accelerate clinical adoption.

Findings

AI achieves expert-level detection of retinopathy of prematurity

Machine learning classifies pediatric cataracts and predicts post-surgical outcomes

AI aids in diagnosing strabismus, myopia, and reading disabilities

Abstract

PURPOSE OF REVIEW: Despite the impressive results of recent artificial intelligence (AI) 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 ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. RECENT FINDINGS: The most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SUMMARY: AI applications could significantly benefit clinical care for pediatric ophthalmology patients 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. Due to 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 implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology. KEYWORDS: pediatric ophthalmology, machine learning, artificial intelligence, deep learning