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Endocrine, Metabolic & Immune Disorders - Drug Targets

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Systematic Review Article

The Application of Artificial Intelligence in Thyroid Nodules: A Systematic Review Based on Bibliometric Analysis

Author(s): Yun Peng, Tong-Tong Wang, Jing-Zhi Wang, Heng Wang, Ruo-Yun Fan, Liang-Geng Gong and Wu-Gen Li*

Volume 24, Issue 11, 2024

Published on: 04 January, 2024

Page: [1280 - 1290] Pages: 11

DOI: 10.2174/0118715303264254231117113456

Price: $65

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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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).

Keywords: Bibliometrics, thyroid nodule, artificial intelligence, radiomics, lymph node metastasis, risk factor.

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