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应用于糖尿病并发症的人工智能：一项文献计量分析。

Artificial intelligence applied to diabetes complications: a bibliometric analysis.

作者信息

Tao Yukun, Hou Jinzheng, Zhou Guangxin, Zhang Da

机构信息

Department of Endocrinology, Air Force Medical Center, Air Force Medical University, Beijing, China.

出版信息

Front Artif Intell. 2025 Jan 31;8:1455341. doi: 10.3389/frai.2025.1455341. eCollection 2025.

DOI:10.3389/frai.2025.1455341
PMID:39959916
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11826422/
Abstract

BACKGROUND AND AIMS: Artificial intelligence (AI)-driven medical assistive technology has been widely used in the diagnosis, treatment and prognosis of diabetes complications. Here we conduct a bibliometric analysis of scientific articles in the field of AI in diabetes complications to explore current research trends and cutting-edge hotspots. METHODOLOGY: On April 20, 2024, we collected and screened relevant articles published from 1988 to 2024 from PubMed. Based on bibliometric tools such as CiteSpace, Vosviewer and bibliometix, we construct knowledge maps to visualize literature information, including annual scientific production, authors, countries, institutions, journals, keywords and research hotspots. RESULTS: A total of 935 articles meeting the criteria were collected and analyzed. The number of annual publications showed an upward trend. Raman, Rajiv published the most articles, and Webster, Dale R had the highest collaboration frequency. The United States, China, and India were the most productive countries. Scientific Reports was the journal with the most publications. The three most frequent diabetes complications were diabetic retinopathy, diabetic nephropathy, and diabetic foot. Machine learning, diabetic retinopathy, screening, deep learning, and diabetic foot are still being researched in 2024. CONCLUSION: Global AI research on diabetes complications is expected to increase further. The investigation of AI in diabetic retinopathy and diabetic foot will be the focus of research in the future.

摘要

背景与目的：人工智能驱动的医疗辅助技术已广泛应用于糖尿病并发症的诊断、治疗和预后。在此，我们对糖尿病并发症领域人工智能方面的科学文章进行文献计量分析，以探索当前的研究趋势和前沿热点。 方法：2024年4月20日，我们从PubMed收集并筛选了1988年至2024年发表的相关文章。基于CiteSpace、Vosviewer和bibliometix等文献计量工具，我们构建知识图谱以可视化文献信息，包括年度科研产出、作者、国家、机构、期刊、关键词和研究热点。 结果：共收集并分析了935篇符合标准的文章。年度发表数量呈上升趋势。拉曼、拉吉夫发表的文章最多，韦伯斯特、戴尔·R合作频率最高。美国、中国和印度是产出最多的国家。《科学报告》是发表文章最多的期刊。最常见的三种糖尿病并发症是糖尿病视网膜病变、糖尿病肾病和糖尿病足。机器学习、糖尿病视网膜病变、筛查、深度学习和糖尿病足在2024年仍在研究中。 结论：全球对糖尿病并发症的人工智能研究预计将进一步增加。对糖尿病视网膜病变和糖尿病足的人工智能研究将是未来的研究重点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/880715c366eb/frai-08-1455341-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/c8d3106525bd/frai-08-1455341-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/1bdf030460d3/frai-08-1455341-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/0be6ced77ca3/frai-08-1455341-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/259a210c1f28/frai-08-1455341-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/0daadd52b9f9/frai-08-1455341-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/3590a52b88a1/frai-08-1455341-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/458770ef2716/frai-08-1455341-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/18fb9cd38942/frai-08-1455341-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/880715c366eb/frai-08-1455341-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/c8d3106525bd/frai-08-1455341-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/1bdf030460d3/frai-08-1455341-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/0be6ced77ca3/frai-08-1455341-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/259a210c1f28/frai-08-1455341-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/0daadd52b9f9/frai-08-1455341-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/3590a52b88a1/frai-08-1455341-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/458770ef2716/frai-08-1455341-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/18fb9cd38942/frai-08-1455341-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab3f/11826422/880715c366eb/frai-08-1455341-g009.jpg

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Artificial intelligence-assisted capsule endoscopy for detecting lesions in Crohn's disease: a systematic review and meta-analysis.

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本文引用的文献

[1]
A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression.

Sci Rep. 2024-12-30

[2]
Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.

Biosensors (Basel). 2024-12-13

[3]
Opportunities to Apply Human-centered Design in Health Care With Artificial Intelligence-based Screening for Diabetic Retinopathy.

Int Ophthalmol Clin. 2024-10-1

[4]
Unveiling the utility of artificial intelligence for prediction, diagnosis, and progression of diabetic kidney disease: an evidence-based systematic review and meta-analysis.

Curr Med Res Opin. 2024-12

[5]
Machine Learning Algorithm-Aided Determination of Predictors of Mortality from Diabetic Foot Sepsis at a Regional Hospital in South Africa During the COVID-19 Pandemic.

Medicina (Kaunas). 2024-10-20

[6]
Enhancing diabetic foot ulcer prediction with machine learning: A focus on Localized examinations.

Heliyon. 2024-9-19

[7]
An explainable Artificial Intelligence software system for predicting diabetes.

Heliyon. 2024-8-10

[8]
Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

Eur J Heart Fail. 2024-10

[9]
Artificial intelligence for diabetes care: current and future prospects.

Lancet Diabetes Endocrinol. 2024-8

[10]
Integrated image-based deep learning and language models for primary diabetes care.

Nat Med. 2024-10

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