Insights into artificial intelligence in myopia management: from a data perspective

被引:4
|
作者
Zhang, Juzhao [1 ]
Zou, Haidong [1 ,2 ,3 ,4 ]
机构
[1] Shanghai Jiao Tong Univ, Shanghai Gen Hosp, Dept Ophthalmol, Sch Med, Shanghai, Peoples R China
[2] Shanghai Eye Hosp, Shanghai Eye Dis Prevent & Treatment Ctr, Shanghai, Peoples R China
[3] Natl Clin Res Ctr Eye Dis, Shanghai, Peoples R China
[4] Shanghai Engn Ctr Precise Diag & Treatment Eye Dis, Shanghai, Peoples R China
关键词
Myopia; Data modality; Artificial intelligence; Machine learning; Deep learning; OPTICAL COHERENCE TOMOGRAPHY; DIABETIC-RETINOPATHY; ADULT-POPULATION; PREVALENCE; TRENDS; MACULOPATHY; VALIDATION; ACCURACY; VISION; FUTURE;
D O I
10.1007/s00417-023-06101-5
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.
引用
收藏
页码:3 / 17
页数:15
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