Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University Nanchang, 330006, China.
The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Endocr Metab Immune Disord Drug Targets. 2024;24(11):1280-1290. doi: 10.2174/0118715303264254231117113456.
Thyroid nodules are common lesions in benign and malignant thyroid diseases. More and more studies have been conducted on the feasibility of artificial intelligence (AI) in the detection, diagnosis, and evaluation of thyroid nodules. The aim of this study was to use bibliometric methods to analyze and predict the hot spots and frontiers of AI in thyroid nodules.
Articles on the application of artificial intelligence in thyroid nodules were retrieved from the Web of Science core collection database. A website (https://bibliometric.com/), VOSviewer and CiteSpace software were used for bibliometric analyses. The collaboration maps of countries and institutions were analyzed. The cluster and timeline view based on cocitation references and keywords citation bursts visualization map were generated.
The study included 601 papers about AI in thyroid nodules. China contributed to more than half (52.41%) of these publications. The cluster view and timeline view of co-citation references were assembled into 9 clusters, "AI", "deep learning", "papillary thyroid carcinoma", "radiomics", "ultrasound image", "biomarkers", "medical image segmentation", "central lymph node metastasis (CLNM)", and "self-organizing auto-encoder". The "AI", "radiomics", "medical image segmentation", "deep learning", and "CLNM", emerging in the last 10 years and continuing until recent years.
An increasing number of scholars were devoted to this field. The potential future research hotspots include risk factor assessment and CLNM prediction of thyroid carcinoma based on radiomics and deep learning, automatic segmentation based on medical images (especially ultrasound images).
甲状腺结节是良性和恶性甲状腺疾病中的常见病变。越来越多的研究已经在人工智能（AI）在甲状腺结节的检测、诊断和评估中的可行性方面进行。本研究旨在使用文献计量学方法分析和预测甲状腺结节中 AI 的热点和前沿。
从 Web of Science 核心集数据库中检索了关于人工智能在甲状腺结节中应用的文章。使用网站（https://bibliometric.com/）、VOSviewer 和 CiteSpace 软件进行文献计量分析。分析了国家和机构的合作图谱。生成了基于共引参考文献和关键词引文突发可视化图谱的聚类和时间线视图。
本研究包括 601 篇关于 AI 在甲状腺结节中的论文。中国在这些出版物中贡献了超过一半（52.41%）。共引参考文献的聚类视图和时间线视图被组装成 9 个聚类，“AI”、“深度学习”、“甲状腺乳头状癌”、“放射组学”、“超声图像”、“生物标志物”、“医学图像分割”、“中央淋巴结转移（CLNM）”和“自组织自动编码器”。“AI”、“放射组学”、“医学图像分割”、“深度学习”和“CLNM”是近 10 年来出现的新兴领域，一直持续到最近几年。
越来越多的学者致力于这一领域。未来潜在的研究热点包括基于放射组学和深度学习的甲状腺癌风险因素评估和 CLNM 预测，以及基于医学图像（尤其是超声图像）的自动分割。
