Artificial intelligence for pediatric ophthalmology

Abstract

Purpose of reviewDespite 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 findingsThe 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. SummaryAI 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.

Automated identification of retinopathy of prematurity by image-based deep learning

“…Convolutional neural networks (CNNs) are DL algorithms commonly applied in image classification, which have been successfully used in the diagnosis of skin cancer [ 8 ], lung cancer [ 9 ], glioma [ 10 ], and breast histopathology [ 11 ]. DL has achieved automated detection of retinal diseases [ 12 , 13 ], including diabetic retinopathy [ 14 ], glaucoma [ 15 ], age-related macular degeneration, and cataracts [ 16 ]. Recently, several studies regarding the diagnosis of ROP with AI have achieved promising results.…”

Artificial Intelligence in Retinopathy of Prematurity Diagnosis

“…As a field, we may need to determine whether explainability or improved outcomes is our primary goal given the black box nature of clinical diagnosis in general. 38 This is analogous to the fear of self-driving cars causing a fatal accident. Technology never will be perfect, but it may be better than the status quo.…”

“…Open access datasets and software could alleviate such issues and encourage timely clinical application for ROP diagnosis. 38 …”

Keratoconus Screening Based on Deep Learning Approach of Corneal Topography

“… 14 The use of artificial intelligence (AI; machine intelligence) or DL has been primarily applied in medical imaging analysis, wherein DL systems have exhibited robust diagnostic performance in detecting various ocular imaging, principally fundus photographs and optical coherence tomography (OCT), 15 in diagnosing or screening diabetic retinopathy (DR), 16 glaucoma, 17 age-related macular degeneration (AMD), 18 and retinopathy of prematurity (ROP). 19 Notably, several methods have been used for automatic diagnosis of ectatic corneal disorders by using corneal topography, such as the discriminant analysis, 6 and then a neural network approach. 20 These approaches achieved a global sensitivity of 94.1% and a global specificity of 97.6% (98.6% for keratoconus alone) in the test set.…”