• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

日本眼部影像注册库:一个全国性的眼科真实世界数据库。

Japan Ocular Imaging Registry: a national ophthalmology real-world database.

作者信息

Miyake Masahiro, Akiyama Masato, Kashiwagi Kenji, Sakamoto Taiji, Oshika Tetsuro

机构信息

Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine, 54 Shogoin, Kawahara, Sakyo, Kyoto, 606-8507, Japan.

Japanese Society of Artificial Intelligence in Ophthalmology, 3-10-5, Nihonbashi, Chuoku, Tokyo, 1038276, Japan.

出版信息

Jpn J Ophthalmol. 2022 Nov;66(6):499-503. doi: 10.1007/s10384-022-00941-0. Epub 2022 Sep 23.

DOI:10.1007/s10384-022-00941-0
PMID:36138192
Abstract

In 2017, the Japanese Ophthalmological Society (JOS) created the Japan Ocular Imaging (JOI) registry, a national database of images and clinical data in the field of ophthalmology in Japan. The JOI registry automatically transfers the information stored in the electronic medical records of each institution to the cloud storage governed by the JOS. This process adheres to Japanese laws and regulations such as the Act on the Protection of Personal Information and the relevant laws of Japan, the Ethical Guidelines for Medical and Biological Research Involving Human Subjects, and the Medical Information System Security Management Guideline of Japan. The JOI registry works closely with academia in ophthalmology, as well as the Japan Association for Medical Informatics, the Japan Ophthalmic Instrument Association, and manufacturers of ophthalmic medical devices. The collected data will be used in studies and surveys to improve the quality of ophthalmic care. The JOI registry has been created as a common asset for the entire ophthalmology field, and welcomes participation of related institutions to utilize the infrastructure to promote multicenter joint research and development. The JOI registry network intends to cover all ophthalmological facilities, including clinics. However, the project is currently being conducted with the cooperation of 22 university hospitals, one private hospital, and two health checkup facilities. As of October 2021, six facilities are connected to the JOI registry network; it comprises a total of 486,189 fundus images, 25,224 optical coherence tomography images, and 11,565 visual field results, among others, are stored in the JOI registry. This project was initially funded by a national funding agency, the Japan Agency for Medical Research and Development (AMED) (ICT Infrastructure Establishment and Implementation of Artificial Intelligence for Clinical and Medical Research, 2017-2019). The General Incorporated Association Japan Ocular Imaging Registry was established in 2019, following which the association is managing the JOI registry under the governance of the JOS. The JOI registry is aiming to become a large-scale real-world database comparable to the Intelligent Research in Sight registry of the United States.

摘要

2017年,日本眼科学会(JOS)创建了日本眼部影像(JOI)登记处,这是一个日本眼科领域图像和临床数据的国家数据库。JOI登记处会自动将各机构电子病历中存储的信息传输到由JOS管理的云存储中。这一过程遵循日本的法律法规,如《个人信息保护法》及日本相关法律、《涉及人类受试者的医学和生物学研究伦理准则》以及日本的《医疗信息系统安全管理指南》。JOI登记处与眼科学术界以及日本医学信息学协会、日本眼科器械协会和眼科医疗设备制造商密切合作。收集到的数据将用于研究和调查,以提高眼科护理质量。JOI登记处已被创建为整个眼科领域的公共资产,并欢迎相关机构参与利用该基础设施来促进多中心联合研发。JOI登记处网络打算覆盖所有眼科设施,包括诊所。然而,该项目目前是在22家大学医院、1家私立医院和2家健康检查机构的合作下进行的。截至2021年10月,有6家机构连接到JOI登记处网络;JOI登记处共存储了486,189张眼底图像、25,224张光学相干断层扫描图像以及11,565份视野检查结果等。该项目最初由日本国立研究开发法人医学研究开发机构(AMED)资助(2017 - 2019年临床和医学研究的信息通信技术基础设施建设及人工智能实施)。日本眼部影像登记处一般社团法人于2019年成立,此后该协会在JOS的管理下负责管理JOI登记处。JOI登记处旨在成为一个与美国的智能视力研究登记处相当的大规模真实世界数据库。

相似文献

1
Japan Ocular Imaging Registry: a national ophthalmology real-world database.日本眼部影像注册库:一个全国性的眼科真实世界数据库。
Jpn J Ophthalmol. 2022 Nov;66(6):499-503. doi: 10.1007/s10384-022-00941-0. Epub 2022 Sep 23.
2
Outpatients flow management and ophthalmic electronic medical records system in university hospital using Yahgee Document View.使用 Yahgee Document View 管理大学医院的门诊流程和眼科电子病历系统。
J Med Syst. 2010 Oct;34(5):883-9. doi: 10.1007/s10916-009-9303-8. Epub 2009 May 15.
3
Ophthalmic Immune-Related Adverse Events after Anti-CTLA-4 or PD-1 Therapy Recorded in the American Academy of Ophthalmology Intelligent Research in Sight Registry.美国眼科学会智能研究视野注册中心记录的抗 CTLA-4 或 PD-1 治疗后的眼部免疫相关不良事件。
Ophthalmology. 2021 Jun;128(6):910-919. doi: 10.1016/j.ophtha.2020.11.001. Epub 2020 Nov 6.
4
[oregis-The German ophthalmological registry].[奥雷吉斯 - 德国眼科登记处]
Ophthalmologie. 2023 Jul;120(7):717-725. doi: 10.1007/s00347-022-01804-2. Epub 2023 Jan 12.
5
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
6
The 2016 American Academy of Ophthalmology IRIS Registry (Intelligent Research in Sight) Database: Characteristics and Methods.2016 年美国眼科学会虹膜注册研究(智能视野研究)数据库:特征和方法。
Ophthalmology. 2018 Aug;125(8):1143-1148. doi: 10.1016/j.ophtha.2017.12.001. Epub 2018 Jan 17.
7
[The importance of informed consent in the field of ophthalmology].[知情同意在眼科领域的重要性]
Hokkaido Igaku Zasshi. 1998 Jan;73(1):15-20.
8
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区,服用抗叶酸抗疟药物的人群中,叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
9
Recent policies that support clinical application of induced pluripotent stem cell-based regenerative therapies.支持基于诱导多能干细胞的再生疗法临床应用的近期政策。
Regen Ther. 2016 Mar 1;4:36-47. doi: 10.1016/j.reth.2016.01.009. eCollection 2016 Jun.
10
Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology.人工智能-机器学习-深度学习算法在眼科学中的应用前景。
Asia Pac J Ophthalmol (Phila). 2019 May-Jun;8(3):264-272. doi: 10.22608/APO.2018479. Epub 2019 May 31.

引用本文的文献

1
Multi-task deep learning for predicting metabolic syndrome from retinal fundus images in a Japanese health checkup dataset.在日本健康检查数据集中,利用多任务深度学习从眼底图像预测代谢综合征
PLoS One. 2025 Aug 21;20(8):e0325337. doi: 10.1371/journal.pone.0325337. eCollection 2025.
2
Association between the retinal age gap and systemic diseases in the Japanese population: the Nagahama study.日本人群视网膜年龄差距与全身性疾病之间的关联:长滨研究
Jpn J Ophthalmol. 2025 Apr 30. doi: 10.1007/s10384-025-01205-3.
3
Study protocol for a prospective natural history registry investigating the relationships between inflammatory markers and disease progression in retinitis pigmentosa: the RP-PRIMARY study.
一项前瞻性自然史登记研究的研究方案,该研究旨在调查色素性视网膜炎中炎症标志物与疾病进展之间的关系:RP-PRIMARY研究。
Jpn J Ophthalmol. 2025 Mar 5. doi: 10.1007/s10384-025-01179-2.
4
Artificial Intelligence Applications in Ophthalmology.人工智能在眼科中的应用。
JMA J. 2025 Jan 15;8(1):66-75. doi: 10.31662/jmaj.2024-0139. Epub 2024 Sep 13.
5
The importance of clinical experience in AI-assisted corneal diagnosis: verification using intentional AI misleading.临床经验在人工智能辅助角膜诊断中的重要性:通过故意的人工智能误导进行验证。
Sci Rep. 2025 Jan 9;15(1):1462. doi: 10.1038/s41598-025-85827-0.
6
Deep learning model for extensive smartphone-based diagnosis and triage of cataracts and multiple corneal diseases.基于深度学习的智能手机广泛诊断和分类白内障及多种角膜疾病的模型。
Br J Ophthalmol. 2024 Sep 20;108(10):1406-1413. doi: 10.1136/bjo-2023-324488.
7
Performance of ChatGPT in Board Examinations for Specialists in the Japanese Ophthalmology Society.ChatGPT在日本眼科学会专科医生资格考试中的表现。
Cureus. 2023 Dec 4;15(12):e49903. doi: 10.7759/cureus.49903. eCollection 2023 Dec.
8
Deep learning-based fundus image analysis for cardiovascular disease: a review.基于深度学习的心血管疾病眼底图像分析综述
Ther Adv Chronic Dis. 2023 Nov 18;14:20406223231209895. doi: 10.1177/20406223231209895. eCollection 2023.