数据与计算发展前沿 ›› 2024, Vol. 6 ›› Issue (2): 117-133.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.02.011

doi: 10.11871/jfdc.issn.2096-742X.2024.02.011

• 技术与应用 • 上一篇    下一篇

机器学习技术在眼健康领域的应用

叶旭1(),杜一1,崔文娟1,沈俊杰2,谢靖2,王露笛1,*()   

  1. 1.中国科学院计算机网络信息中心，北京 100083
    2.爱尔眼科医院集团股份有限公司，湖南 长沙 410015
  • 收稿日期:2023-04-06 出版日期:2024-04-20 发布日期:2024-04-26
  • 通讯作者: *王露笛（E-mail: wld@cnic.cn）
  • 作者简介:叶旭，中国科学院计算机网络信息中心，硕士研究生，主要研究方向为机器学习、数据挖掘等。
    本文负责综述路线的设计及部分内容撰写。
    YE Xu is a master's student at the Computer Network Information Center of the Chinese Academy of Sciences. His main research interests include machine learning and data mining.
    In this paper, he is responsible for the design of the overview route and partial content writing.
    E-mail: yexu@cnic.cn|王露笛，中国科学院计算机网络信息中心，高级工程师，主要研究方向为大数据、人工智能等。
    本文负责总体统稿及论文的审阅与修改。
    WANG Ludi is a senior engineer at the Computer Network Information Center of the Chinese Academy of Sciences. His main research interests include big data and artificial intelligence.
    In this paper, she is responsible for the overall drafting and review and revision of the paper.
    E-mail: wld@cnic.cn
  • 基金资助:
    中国科学院科技服务网络计划（STS计划）区域重点项目(KFJ-STS-QYZD-2021-11-001)

Application of Machine Learning Technology in the Field of Eye Health

YE Xu1(),DU Yi1,CUI Wenjuan1,SHEN Junjie2,XIE Jing2,WANG Ludi1,*()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
    2. Changsha Aier Eye Hospital, Changsha, Hunan 410015, China
  • Received:2023-04-06 Online:2024-04-20 Published:2024-04-26

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摘要：

【应用背景】随着数据的爆炸式增长、算法的不断改进以及计算能力的快速发展，机器学习在教育、金融、制造和医疗等领域均得到了广泛应用。在眼健康领域，机器学习也已经在疾病诊断、疾病分级、医学检查和疾病早期筛查等许多任务上实现了初步应用。【方法】本文通过对眼健康领域国内外相关文献的调研，从眼科疾病类别、就诊阶段、数据类型及技术类型4个不同维度对领域应用进行了梳理与分析，并对接下来的研究做出相应的展望。【结果】基于调研分析的结果可以看出，在眼健康领域中，机器学习技术主要以图像数据为主，围绕疾病诊断与分级展开。同时在疾病早期筛查和疾病风险预测等处于疾病发展早期阶段的任务上也取得了不错的表现。【结论】通过将机器学习技术应用到眼科诊疗过程的各个阶段，有望降低眼科医生负担、提升眼科医生工作效率、帮助控制患者病情发展、提升患者生活质量并降低患者治疗的经济成本和时间成本。

关键词: 眼健康, 机器学习, 大数据技术

Abstract:

[Background] With the explosive growth of data, continuous improvement of algorithms, and rapid development of computing power, machine learning has been widely used in education, finance, manufacturing, and medical fields. In the field of eye health, machine learning has also achieved preliminary applications in many tasks such as disease diagnosis, disease grading, medical examination, and early screening of diseases. [Methods] Based on the investigation of relevant domestic and foreign literature in the field of eye health, this paper sorts out and analyzes the application of the field from four different dimensions of ophthalmic disease category, treatment stage, data type, and technology type, and makes corresponding prospects for the next research. [Results] Based on the results of research and analysis, it can be seen that in the field of eye health, machine learning technology mainly uses image data, focusing on disease diagnosis and grading. At the same time, it has also achieved good performance in tasks such as early disease screening and disease risk prediction that are in the early stages of disease development. [Conclusion] By applying machine learning technology to all stages of the ophthalmology diagnosis and treatment process, it is expected to reduce the burden on ophthalmologists, improve the work efficiency of ophthalmologists, help control the development of patients' diseases, improve the quality of life of the patients, and reduce the economic and time costs of patient treatment.

Key words: eye health, machine learning, big data