Artificial intelligence in pediatric myopia - a narrative review

Neelam Pawar; Tos T. J. M. Berendschot; Noel J. C. Bauer; R. Meenakshi; Devendra Maheshwari; Binh Duong Giap; Nambi Nallasamy

doi:10.1007/s00417-026-07193-5

Artificial intelligence in pediatric myopia - a narrative review

Neelam Pawar^*
, Tos T. J. M. Berendschot
, Noel J. C. Bauer
, R. Meenakshi
, Devendra Maheshwari
, Binh Duong Giap
, Nambi Nallasamy

^*Corresponding author for this work

Research output: Contribution to journal › (Systematic) Review article › peer-review

Abstract

This review examines the promising potential of Artificial Intelligence (AI) in pediatric ophthalmology, specifically in the assessment, prediction, management, and treatment of myopia in children. The use of AI, particularly machine learning (ML) and deep learning (DL), in predicting myopia in children has garnered significant interest for its potential in early screening, detection, prognosis prediction, monitoring of anti-myopia treatment, personalized interventions, and proac-tive management strategies. This review aims to summarize the current literature on myopia and AI, presenting them as emerging trends and future directions in the pediatric population, and highlighting emerging strategies for the future of myopia management

Original language	English
Number of pages	32
Journal	Graefe's Archive for Clinical and Experimental Ophthalmology
DOIs	https://doi.org/10.1007/s00417-026-07193-5
Publication status	E-pub ahead of print - 1 Mar 2026

Keywords

Artificial Intelligence
Machine learning
Deep learning
Pediatric myopia
AXIAL LENGTH
CHILDREN
PREVALENCE
ASSOCIATION
PROGRESSION
PREDICTION
REFRACTION
ETIOLOGY
TRENDS
ONSET

Access to Document

10.1007/s00417-026-07193-5

Cite this

@article{82a707a58a74458888f5067cfd9e36b9,

title = "Artificial intelligence in pediatric myopia - a narrative review",

abstract = "This review examines the promising potential of Artificial Intelligence (AI) in pediatric ophthalmology, specifically in the assessment, prediction, management, and treatment of myopia in children. The use of AI, particularly machine learning (ML) and deep learning (DL), in predicting myopia in children has garnered significant interest for its potential in early screening, detection, prognosis prediction, monitoring of anti-myopia treatment, personalized interventions, and proac-tive management strategies. This review aims to summarize the current literature on myopia and AI, presenting them as emerging trends and future directions in the pediatric population, and highlighting emerging strategies for the future of myopia management",

keywords = "Artificial Intelligence, Machine learning, Deep learning, Pediatric myopia, AXIAL LENGTH, CHILDREN, PREVALENCE, ASSOCIATION, PROGRESSION, PREDICTION, REFRACTION, ETIOLOGY, TRENDS, ONSET",

author = "Neelam Pawar and Berendschot, \{Tos T. J. M.\} and Bauer, \{Noel J. C.\} and R. Meenakshi and Devendra Maheshwari and Giap, \{Binh Duong\} and Nambi Nallasamy",

year = "2026",

month = mar,

day = "1",

doi = "10.1007/s00417-026-07193-5",

language = "English",

journal = "Graefe's Archive for Clinical and Experimental Ophthalmology",

issn = "0721-832X",

publisher = "Springer",

}

TY - JOUR

T1 - Artificial intelligence in pediatric myopia - a narrative review

AU - Pawar, Neelam

AU - Berendschot, Tos T. J. M.

AU - Bauer, Noel J. C.

AU - Meenakshi, R.

AU - Maheshwari, Devendra

AU - Giap, Binh Duong

AU - Nallasamy, Nambi

PY - 2026/3/1

Y1 - 2026/3/1

N2 - This review examines the promising potential of Artificial Intelligence (AI) in pediatric ophthalmology, specifically in the assessment, prediction, management, and treatment of myopia in children. The use of AI, particularly machine learning (ML) and deep learning (DL), in predicting myopia in children has garnered significant interest for its potential in early screening, detection, prognosis prediction, monitoring of anti-myopia treatment, personalized interventions, and proac-tive management strategies. This review aims to summarize the current literature on myopia and AI, presenting them as emerging trends and future directions in the pediatric population, and highlighting emerging strategies for the future of myopia management

AB - This review examines the promising potential of Artificial Intelligence (AI) in pediatric ophthalmology, specifically in the assessment, prediction, management, and treatment of myopia in children. The use of AI, particularly machine learning (ML) and deep learning (DL), in predicting myopia in children has garnered significant interest for its potential in early screening, detection, prognosis prediction, monitoring of anti-myopia treatment, personalized interventions, and proac-tive management strategies. This review aims to summarize the current literature on myopia and AI, presenting them as emerging trends and future directions in the pediatric population, and highlighting emerging strategies for the future of myopia management

KW - Artificial Intelligence

KW - Machine learning

KW - Deep learning

KW - Pediatric myopia

KW - AXIAL LENGTH

KW - CHILDREN

KW - PREVALENCE

KW - ASSOCIATION

KW - PROGRESSION

KW - PREDICTION

KW - REFRACTION

KW - ETIOLOGY

KW - TRENDS

KW - ONSET

U2 - 10.1007/s00417-026-07193-5

DO - 10.1007/s00417-026-07193-5

M3 - (Systematic) Review article

SN - 0721-832X

JO - Graefe's Archive for Clinical and Experimental Ophthalmology

JF - Graefe's Archive for Clinical and Experimental Ophthalmology

ER -

Artificial intelligence in pediatric myopia - a narrative review

Abstract

Keywords

Access to Document

Fingerprint

Cite this