Singapore National Eye Centre, Singapore.
Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University.
Curr Opin Ophthalmol. 2024 Nov 1;35(6):463-471. doi: 10.1097/ICU.0000000000001086. Epub 2024 Aug 26.
Myopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management.
The bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment.
Artificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.
近视是全球视力损害的主要原因之一，因此近视及其并发症给医疗保健和经济带来了沉重负担。由于大多数近视发生在儿童时期，因此早期实施干预措施以减缓近视进展最为有效。为了解决这一公共卫生挑战，人工智能已成为儿童近视管理的潜在解决方案。
人工智能在儿童近视方面的研究主要集中在用于识别近视进展高风险儿童的传统机器学习模型上。最近，随着更多的数据集、更多的计算能力和更复杂的计算模型，利用人工智能进行包括使用大数据、多模态数据进行大规模近视筛查、推进近视进展成像技术以及进行精准治疗的深度学习模型等新型方法的文献大量涌现。
人工智能在儿童近视管理领域具有巨大的应用潜力。新的人工智能模式，包括自动化机器学习、大型语言模型和联邦学习，通过提供精准医疗、提高健康素养以及允许数据隐私保护，未来可能会发挥重要作用。然而，这些技术进步带来了实际挑战，包括监管和临床整合。
