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Machine Learning Applications in Pediatric Ophthalmology.

Isdin Oke^1,2, Deborah VanderVeen^1,2

¹Department of Ophthalmology, Boston Children's Hospital, Boston, MA, USA.

Seminars in Ophthalmology

|March 1, 2021

Summary

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Machine learning (ML) is increasingly used in pediatric ophthalmology for diagnosing and treating visual development disorders. Further understanding of ML principles can unlock new diagnostic and treatment applications for eye care providers.

Area of Science:

Ophthalmology
Machine Learning
Pediatric Eye Care

Background:

Machine learning (ML) applications in pediatric ophthalmology are rapidly evolving.
Current research primarily focuses on retinopathy of prematurity.
Emerging efforts target amblyogenic conditions like cataracts, strabismus, and refractive errors.

Purpose of the Study:

To review emerging machine learning applications in pediatric ophthalmology.
To highlight ML's role in diagnosing and treating visual development disorders.
To identify future research directions for ML in the subspecialty.

Main Methods:

A comprehensive literature review was conducted.
Studies applying machine learning algorithms to pediatric ophthalmology problems were analyzed.

Keywords:

Pediatric ophthalmology artificial intelligence deep learning machine learning visual development

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Main Results:

Retinopathy of prematurity is a major focus of current ML research in this field.
Increasing ML applications are noted for diagnosing pediatric cataracts, strabismus, and high refractive errors.
The potential for ML in addressing other visual development disorders is significant.

Conclusions:

Machine learning offers promising tools for pediatric ophthalmology.
A deeper understanding of ML principles will empower eye care providers.
This knowledge can drive innovation in diagnosing and treating complex visual conditions.