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AI-enhanced digital technologies for myopia management: advancements, challenges, and future prospects.

Authors :
Ali, Saba Ghazanfar
Zhang, Chenxi
Guan, Zhouyu
Chen, Tingli
Wu, Qiang
Li, Ping
Yang, Po
Ghazanfar, Zainab
Jung, Younhyun
Chen, Yuting
Sheng, Bin
Tham, Yih-Chung
Wang, Xiangning
Wen, Yang
Source :
Visual Computer; Jun2024, Vol. 40 Issue 6, p3871-3887, 17p
Publication Year :
2024

Abstract

Myopia, a prevalent refractive error leading to visual impairment, presents a substantial global health challenge. Current preventive measures, including pharmaceutical interventions and optical corrections, require significant resources and a dedicated workforce. Digital healthcare technology, encompassing artificial intelligence (AI) and telemedicine, offers promising solutions by delivering scalable, portable, and dependable approaches. Inspired by the success of deep learning systems in ophthalmology, these digital health technologies can revolutionize myopia management, enhancing automation, scalability, and remote monitoring. In this comprehensive review, we explore the potential applications of digital healthcare technology in measuring, preventing, controlling, and managing myopia. We highlight specific focus areas, emphasize the advancements and benefits of digital healthcare, and discuss the challenges associated with the clinical implementation of digital devices and AI applications in myopia management. Realizing the full potential of digital technologies in myopia management necessitates addressing access barriers, seamless integration with digital healthcare systems, validation of their effectiveness, and fostering user engagement. While adoption challenges persist, harnessing digital healthcare solutions can lead to improved vision, reduced myopia-related difficulties, and overall enhanced eye health. As this field advances, it holds a promising future for individuals affected by myopia. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01782789
Volume :
40
Issue :
6
Database :
Complementary Index
Journal :
Visual Computer
Publication Type :
Academic Journal
Accession number :
177714384
Full Text :
https://doi.org/10.1007/s00371-024-03391-6