人工智能应用于糖尿病视网膜病变的文献计量学分析

doi:10.12114/j.issn.1007-9572.2022.0851

中国全科医学 ›› 2023, Vol. 26 ›› Issue (15): 1847-1856.DOI: 10.12114/j.issn.1007-9572.2022.0851

所属专题：内分泌代谢性疾病最新文章合辑；数智医疗最新文章合辑

人工智能应用于糖尿病视网膜病变的文献计量学分析

刘春¹, 蹇文渊², 段俊国²^,³^,^*()

1．610075　四川省成都市，成都中医药大学
2．610084　四川省成都市，成都中医大银海眼科医院
3．610075　四川省成都市，中医药眼病防治与视功能保护四川省重点实验室

收稿日期:2022-12-16 修回日期:2023-01-24 出版日期:2023-05-20 发布日期:2022-12-20
通讯作者: 段俊国
作者贡献：刘春进行文章的构思与设计，研究的实施、结果分析及解释，文章撰写及修订；蹇文渊负责绘制图表及数据收集与整理；段俊国负责文章的最后修订、质量控制及审校，对文章整体负责。
基金资助:
国家自然科学基金面上项目--基于光谱图像技术的眼底色与舌色的相关性研究(82074335); 四川省科技计划项目--视网膜图像技术在慢病防控中的应用示范(2021ZHYZ0017)

Bibliometric Analysis of Artificial Intelligence in Diabetic Retinopathy

LIU Chun¹, JIAN Wenyuan², DUAN Junguo²^,³^,^*()

1. Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China
2. Ineye Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu 610084, China
3. Key Laboratory of Sichuan Province Ophthalmopathy Prevention & Cure and Visual Function Protection, Chengdu 610075, China

Received:2022-12-16 Revised:2023-01-24 Published:2023-05-20 Online:2022-12-20
Contact: DUAN Junguo

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摘要/Abstract

摘要： 背景近年来，人工智能（AI）在医学领域发展迅速，在糖尿病视网膜病变（DR）中的应用范围不断扩展。目的通过文献计量分析总结AI在DR领域的应用情况，阐明AI在DR领域相关研究的现状、热点和新兴趋势，以期为未来的研究提供思路。方法以Web of Science数据库为来源，检索建库至2022-11-04的AI应用于DR领域的相关文献，运用CiteSpace软件对纳入文献进行发文量、国家、机构、作者、共被引和关键词的文献计量学分析。结果共获得1 770篇文献，2011年1月至2022年11月发文量总体呈上升趋势，2021年发文量达峰值（402篇）。中国是发文量（440篇）位居第1的国家，英国为中心性（0.26）最高的国家。机构合作网络图谱共纳入436家机构，以中山大学和首都医科大学为代表。作者合作网络图谱共纳入601位作者，以JIA Y L和HWANG T为代表。GULSHAN V、ABRàMOFF M D与TING D W 3位高被引作者对该领域做出了重要贡献。Ophthalmology、Invest Ophth Vis Sci和Ieee T Med Imaging是AI应用于DR领域的相关研究最具影响力的3大期刊。AI应用于DR研究的热点领域主要集中在病灶分割和DR诊断方面。对DR并发症糖尿病性黄斑水肿的疗效预测、DR病程管理以及AI算法性能提高可能是未来的研究趋势。结论研究者可参考本研究所示的研究热点及趋势，重点关注AI在DR诊断、病程管理与AI算法性能提高方面的相关问题。

关键词: 糖尿病视网膜病变, 人工智能, CiteSpace, 文献计量分析, 可视化

Abstract:

Background

In recent years, artificial intelligence (AI) has shown rapid development in the medical field, and its application in diabetic retinopathy (DR) has been expanding.

Objective

To summarize the application of AI in DR through bibliometric analysis and elucidate the current status, hot spots and emerging trends of AI-related research in DR, with a view to providing ideas for future research.

Methods

The research was performed on the Web of Science database for the researches related to AI applications in DR from inception to 2022-11-04 and used CiteSpace software to conduct bibliometric analysis of the number of articles, countries, institutions, authors, co-citation and keywords in the literature.

Results

A total of 1 770 papers were obtained, with an overall increasing trend in the number of publications and a peak of 402 papers in 2021. China was the top country in terms of the number of publications (440), and the UK was the country with the highest intermediary centrality (0.26). A total of 436 institutions were included in the institutional collaboration network mapping, represented by Sun Yat-sen University and Capital Medical University. A total of 601 authors were included in the author collaboration network mapping, represented by JIA Y L and HWANG T. Three highly cited authors, GULSHAN V, ABRàMOFF M D and TING D W, have made important contributions to the field. Ophthalmology, Invest Ophth Vis Sci and Ieee T Med Imaging are the three most influential journals in the field of AI applied to DR. The research hot spots were mainly focused on lesion segmentation and DR diagnosis. The future research trends may be efficacy prediction of diabetic macular edema as a complication of DR, disease management and improvement of AI algorithm performance.

Conclusion

Researchers can refer to the research hot spots and trends shown by this bibliometric analysis, focusing on AI in DR diagnosis, disease management and improvement of AI algorithm performance.

Key words: Diabetic retinopathy, Artificial intelligence, CiteSpace, Bibliometric analysis, Visualization

刘春,蹇文渊,段俊国. 人工智能应用于糖尿病视网膜病变的文献计量学分析[J]. 中国全科医学, 2023, 26(15): 1847-1856. DOI: 10.12114/j.issn.1007-9572.2022.0851.
LIU Chun,JIAN Wenyuan,DUAN Junguo. Bibliometric Analysis of Artificial Intelligence in Diabetic Retinopathy[J]. Chinese General Practice, 2023, 26(15): 1847-1856. DOI: 10.12114/j.issn.1007-9572.2022.0851.

图/表 11

参考文献 60

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序号	被引作者	被引频次（次）	中介中心性
1	GULSHAN V	412	0.03
2	ABRÀMOFF M D	363	0.17
3	TING D W	285	0.04
4	NIEMEIJER M	240	0.02
5	HE K	201	0.04
6	QUELLEC G	179	0.04
7	GARGEYA R	176	0.02
8	SZEGEDY C	172	0.03
9	DECENCIERE E	164	0.04
10	KRIZHEVSKY A	153	0.03

序号	被引作者	被引频次（次）	中介中心性
1	GULSHAN V	412	0.03
2	ABRÀMOFF M D	363	0.17
3	TING D W	285	0.04
4	NIEMEIJER M	240	0.02
5	HE K	201	0.04
6	QUELLEC G	179	0.04
7	GARGEYA R	176	0.02
8	SZEGEDY C	172	0.03
9	DECENCIERE E	164	0.04
10	KRIZHEVSKY A	153	0.03

序号	被引期刊	JCR分区	IF	被引频次	中介中心性
1	Ophthalmology	Q1	14.28	791	0.02
2	Invest Ophth Vis Sci	Q1	4.93	711	0.01
3	Ieee T Med Imaging	Q1	11.04	628	0.01
4	Brit J Ophthalmol	Q1	5.91	576	0.01
5	Jama-J Am Med Assoc	Q1	157.38	526	0.03
6	Med Image Anal	Q1	13.83	432	0.01
7	Diabetes Care	Q1	17.16	431	0.01
8	Plos One	Q2	3.75	413	0.02
9	Am J Ophthalmol	Q1	5.49	403	0.01
10	Lect Notes Comput Sc	未查到	—	391	0.03

序号	被引期刊	JCR分区	IF	被引频次	中介中心性
1	Ophthalmology	Q1	14.28	791	0.02
2	Invest Ophth Vis Sci	Q1	4.93	711	0.01
3	Ieee T Med Imaging	Q1	11.04	628	0.01
4	Brit J Ophthalmol	Q1	5.91	576	0.01
5	Jama-J Am Med Assoc	Q1	157.38	526	0.03
6	Med Image Anal	Q1	13.83	432	0.01
7	Diabetes Care	Q1	17.16	431	0.01
8	Plos One	Q2	3.75	413	0.02
9	Am J Ophthalmol	Q1	5.49	403	0.01
10	Lect Notes Comput Sc	未查到	—	391	0.03

人工智能应用于糖尿病视网膜病变的文献计量学分析

Bibliometric Analysis of Artificial Intelligence in Diabetic Retinopathy

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序号	文章标题	第一作者	发表年份（年）	共被引频次（次）
1	Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs^[15]	GULSHAN V	2016	354
2	Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations with Diabetes^[16]	TING D W	2017	218
3	Automated Identification of Diabetic Retinopathy Using Deep Learning^[17]	GARGEYA R	2017	175
4	Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning^[18]	ABRÀMOFF M D	2016	127
5	Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices^[19]	ABRÀMOFF M D	2018	74
6	Deep learning^[20]	LECUN Y	2015	69
7	Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning^[21]	KERMANY D S	2018	61
8	Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning^[22]	POPLIN R	2018	61
9	Clinically applicable deep learning for diagnosis and referral in retinal disease^[23]	DE F	2018	59
10	Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy^[24]	KRAUSE J	2018	58

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