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人工智能在近视防控中的应用。

Application of artificial intelligence in myopia prevention and control.

作者信息

Liu Nan, Li Li, Yu Jifeng

机构信息

Department of Ophthalmology Beijing Children's Hospital Capital Medical University National Center for Children's Health Key Laboratory of Major Diseases in Children Ministry of Education Beijing China.

出版信息

Pediatr Investig. 2025 Mar 18;9(2):114-124. doi: 10.1002/ped4.70001. eCollection 2025 Jun.

DOI:10.1002/ped4.70001
PMID:40539006
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12175636/
Abstract

The global incidence of myopia is increasing, and high myopia increases the risk of pathological myopia, which can lead to irreversible visual impairment, posing a significant global health concern. Artificial intelligence (AI) may be a solution to the myopia pandemic, with potential applications in early identification, risk stratification, progression prediction, and timely intervention to address unmet needs. AI has been developed to detect, diagnose, and predict the progression of myopia in both children and adults. In this review, the current state of AI technology applications in the field of myopia has been comprehensively reviewed, and the challenges, current development status, and future directions of AI have also been discussed, which hold great significance for the further application of AI in myopia management.

摘要

全球近视发病率正在上升,高度近视会增加病理性近视的风险,而病理性近视可导致不可逆的视力损害,这成为一个重大的全球健康问题。人工智能(AI)可能是解决近视大流行的一种方法,在早期识别、风险分层、进展预测以及满足未得到满足的需求的及时干预方面具有潜在应用价值。人工智能已被开发用于检测、诊断和预测儿童及成人近视的进展。在本综述中,全面回顾了人工智能技术在近视领域的应用现状,并讨论了人工智能面临的挑战、当前发展状况及未来方向,这对于人工智能在近视管理中的进一步应用具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba5b/12175636/d9e6c371ddb9/PED4-9-114-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba5b/12175636/d9e6c371ddb9/PED4-9-114-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba5b/12175636/d9e6c371ddb9/PED4-9-114-g001.jpg

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本文引用的文献

1
A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children.一种用于儿童近视发病预测及干预效果评估的深度学习系统。
NPJ Digit Med. 2024 Aug 7;7(1):206. doi: 10.1038/s41746-024-01204-7.
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Role of Machine Learning and Artificial Intelligence in the Diagnosis and Treatment of Refractive Errors for Enhanced Eye Care: A Systematic Review.机器学习和人工智能在屈光不正诊断与治疗中对强化眼保健的作用:一项系统综述
Cureus. 2024 Apr 6;16(4):e57706. doi: 10.7759/cureus.57706. eCollection 2024 Apr.
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Development and validation of predictive models for myopia onset and progression using extensive 15-year refractive data in children and adolescents.
利用儿童和青少年长达 15 年的全面屈光数据,开发和验证近视发病和进展的预测模型。
J Transl Med. 2024 Mar 17;22(1):289. doi: 10.1186/s12967-024-05075-0.
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Front Public Health. 2024 Feb 22;12:1352759. doi: 10.3389/fpubh.2024.1352759. eCollection 2024.
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Study of myopia progression and risk factors in Hubei children aged 7-10 years using machine learning: a longitudinal cohort.基于机器学习的湖北省 7-10 岁儿童近视进展及危险因素研究:一项纵向队列研究。
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Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.利用多元自适应回归样条识别和探索阿托品控制下近视儿童眼压预测的影响因素。
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Development and validation of a deep learning model to predict axial length from ultra-wide field images.用于从超广角图像预测眼轴长度的深度学习模型的开发与验证
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BMC Ophthalmol. 2023 Nov 27;23(1):487. doi: 10.1186/s12886-023-03231-6.
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Deep neural network with self-attention based automated determination system for treatment zone and peripheral steepened zone in Orthokeratology for adolescent myopia.基于自注意力的深度神经网络的自动确定系统,用于青少年近视的角膜塑形术的治疗区和周边陡峭区。
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Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes.机器学习模型预测高度近视眼中的长期视力
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