信息通信技术与政策

信息通信技术与政策

信息通信技术与政策 ›› 2023, Vol. 49 ›› Issue (9): 87-91.doi: 10.12267/j.issn.2096-5931.2023.09.013

技术与标准 上一篇    下一篇

机器学习在眼科疾病辅助诊疗中的应用及监管*

Application and regulation of machine learning in auxiliary diagnosis and treatment of ophthalmic diseases

李静雯1, 王令珑2, 赵阳光1, 崔伟男1   

  1. 1.中国信息通信研究院云计算与大数据研究所,北京 100191
    2.香港理工大学,中国香港 100872
  • 收稿日期:2023-03-06 出版日期:2023-09-25 发布日期:2023-09-27
  • 作者简介:
    李静雯,中国信息通信研究院云计算与大数据研究所工程师,博士,主要从事医疗人工智能等方面的研究工作
    王令珑,香港理工大学,硕士研究生在读,主要从事数字健康等方面的研究工作
    赵阳光,中国信息通信研究院云计算与大数据研究所生物技术部副主任,工程师,主要从事人工智能医疗器械领域等方面的研究工作
    崔伟男,中国信息通信研究院云计算与大数据研究所生物技术部副主任,高级工程师,主要从事医疗人工智能等方面的研究工作
  • 基金资助:
    *国家重点研发计划(2020YFC2008206)

LI Jingwen1, WANG Linglong2, ZHAO Yangguang1, CUI Weinan1   

  1. 1. Cloud Computing & Big Data Research Institute, China Academy of Information and Communications Technology, Beijing 100191, China
    2. The Hong Kong Polytechnic University, Hong Kong 100872, China
  • Received:2023-03-06 Online:2023-09-25 Published:2023-09-27

摘要:

眼科疾病患病人群庞大,患者若不能得到及时治疗,严重会导致残疾,这不仅影响患者的生活质量,也会给社会带来沉重的医疗负担。同时,由于眼科医师的数量不足以及基层医疗机构在眼科诊疗设施和技术方面与三甲医院存在一定差异,我国正面临医疗资源分配不均的现状。而机器学习技术对于提高眼科筛查能力和实现疾病的早期干预具有重要研究意义。分析并总结了机器学习技术在眼科疾病辅助诊疗中的应用现状,并针对该类医疗器械的特点,分析其监管难点并给出发展建议。

关键词: 机器学习, 深度学习, 辅助诊疗, 眼科疾病, 医疗器械

Abstract:

A huge number of patients suffer from ophthalmic diseases, which can lead to disability if left untreated. The ophthalmic diseases not only affect life quality of patients, but also cause a heavy medical burden on society. Meanwhile, there is a shortage of ophthalmologists, and there are differences in ophthalmic treatment facilities and technologies between primary medical institutions and tertiary hospitals. China is currently facing an unequal allocation of medical resources. The machine learning technology has important research significance for improving the ability of ophthalmology screening and realizing early intervention of diseases. This paper analyzes and summarizes the application status of machine learning technology in the auxiliary diagnosis and treatment of ophthalmic diseases. Then, according to the characteristics of medical equipment using machine technologies, it analyzes the regulatory difficulties and puts forward development suggestions.

Key words: machine learning, deep learning, auxiliary diagnosis, ophthalmic diseases, medical equipment

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