Bibliometric Analysis of Intelligent Ultrasound Imaging in the Diagnosis of Thyroid Nodules

Yang LI, Jianlin WANG, Jiaojiao MA, Zhe SUN, Bo ZHANG

Acta Academiae Medicinae Sinicae ›› 2025, Vol. 47 ›› Issue (4) : 590-600.

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Abbreviation (ISO4): Acta Academiae Medicinae Sinicae      Editor in chief: Xuetao CAO

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Acta Academiae Medicinae Sinicae ›› 2025, Vol. 47 ›› Issue (4) : 590-600. DOI: 10.3881/j.issn.1000-503X.16324
Original Articles

Bibliometric Analysis of Intelligent Ultrasound Imaging in the Diagnosis of Thyroid Nodules

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Abstract

Objective To explore the research progress and hotspots of intelligent ultrasound imaging in the diagnosis of thyroid nodules and clarify the research directions via the bibliometric method.Methods The relevant research articles on intelligent ultrasound imaging in the diagnosis of thyroid nodules were retrieved from the Web of Science Core Collection,covering the period from January 2004 to August 2024.Python was used to analyze the number of annual publications.VOSviewer was used to create the co-occurrence network of authors and the keyword density map.CiteSpace was used to demonstrate the dual-map overlays of the journals,as well as the bursts and clustering of co-citations and keywords.Results A total of 1 179 articles were included.The annual number of publications increased steadily.The involved journals demonstrated high quality,and the publications showed a trend of cross-research.Chinese researchers were the core research force in this field.Haugen et al.’s study on the guidelines for thyroid nodules had the most citations.The clustering of co-citations and keywords indicated studies in multiple fields.Thyroid nodules,cancer,and deep learning were the representative keywords in this field.Conclusions The continuous enrichment of research topics promotes the rapid development of intelligent ultrasound imaging for thyroid nodules.Intelligent diagnosis methods based on deep learning can provide diagnostic suggestions,while there are still challenges such as interpretation.One of the research directions is the deep combination of intelligent diagnosis algorithms and medical knowledge.

Key words

thyroid nodules / intelligent diagnosis / ultrasound images / bibliometrics

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Yang LI , Jianlin WANG , Jiaojiao MA , et al . Bibliometric Analysis of Intelligent Ultrasound Imaging in the Diagnosis of Thyroid Nodules[J]. Acta Academiae Medicinae Sinicae. 2025, 47(4): 590-600 https://doi.org/10.3881/j.issn.1000-503X.16324

References

[1]
Alexander EK, Cibas ES. Diagnosis of thyroid nodules[J]. Lancet Diabetes Endocrinol, 2022, 10(7):533-539.DOI:10.1016/S2213-8587(22)00101-2.
[2]
Na DG, Lee JH, Jung SL, et al. Radiofrequency ablation of benign thyroid nodules and recurrent thyroid cancers:consensus statement and recommendations[J]. Korean J Radiol, 2012, 13(2):117-125.DOI:10.3348/kjr.2012.13.2.117.
[3]
Wu M. A correlation study between thyroid imaging report and data systems and the Bethesda system for reporting thyroid cytology with surgical follow-up-an ultrasound-trained cytopathologist’s experience[J]. Diagn Cytopathol, 2021, 49(4):494-499.DOI:10.1002/dc.24644.
[4]
Gong ZJ, Xin J, Yin J, et al. Diagnostic value of artificial intelligence-assistant diagnostic system combined with contrast-enhanced ultrasound in thyroid TI-RADS 4 nodules[J]. J Ultrasound Med, 2023, 42(7):1527-1535.DOI:10.1002/jum.16170.
[5]
Handelman GS, Kok HK, Chandra RV, et al. eDoctor:machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6):603-619.DOI:10.1111/joim.12822.
[6]
Wang L, Zhou X, Nie X, et al. A multi-scale densely connected convolutional neural network for automated thyroid nodule classification[J]. Front Neurosci, 2022,16:878718.DOI:10.3389/fnins.2022.878718.
[7]
Vanithamani R, Dhivya R. International conference on soft computing and pattern recognition[C]. Cham: Springer International Publishing,2016:509-514.DOI:10.1007/978-3-319-60618-7_50.
[8]
Yu X, Wang H, Ma L. Detection of thyroid nodules with ultrasound images based on deep learning[J]. Curr Med Imaging Rev, 2020, 16(2):174-180.DOI:10.2174/1573405615666191023104751.
[9]
He LT, Chen FJ, Zhou DZ, et al. A comparison of the performances of artificial intelligence system and radiologists in the ultrasound diagnosis of thyroid nodules[J]. Curr Med Imaging, 2022, 18(13):1369-1377.DOI:10.2174/1573405618666220422132251.
[10]
Nguyen DT, Kang JK, Pham TD, et al. Ultrasound image-based diagnosis of malignant thyroid nodule using artificial intelligence[J]. Sensors, 2020, 20(7):1822.DOI:10.3390/s20071822.
[11]
Liu R, Zhou S, Guo Y, et al. Nodule localization in thyroid ultrasound images with a joint-training convolutional neural network[J]. J Digit Imaging, 2020, 33(5):1266-1279.DOI:10.1007/s10278-020-00366-6.
[12]
Kim K, Chon N, Jeong HW, et al. Improvement of ultrasound image quality using non-local means noise-reduction approach for precise quality control and accurate diagnosis of thyroid nodules[J]. Int J Environ Res Public Health, 2022, 19(21):13743.DOI:10.3390/ijerph192113743.
[13]
Ma J, Kong D, Wu F, et al. Densely connected convolutional networks for ultrasound image based lesion segmentation[J]. Comput Biol Med, 2024,168:107725.DOI:10.1016/j.compbiomed.2023.107725.
[14]
Poudel P, Illanes A, Ataide EJG, et al. Thyroid ultrasound texture classification using autoregressive features in conjunction with machine learning approaches[J]. IEEE Access, 2019, 7:79354-79365.DOI:10.1109/access.2019.2923547.
[15]
Yu Z, Liu S, Liu P, et al. Automatic detection and diagnosis of thyroid ultrasound images based on attention mechanism[J]. Comput Biol Med, 2023,155:106468.DOI:10.1016/j.compbiomed.2022.106468.
[16]
Liu N, Fenster A, Tessier D, et al. Self-supervised enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation[J]. Phys Med Biol, 2023, 68(23):235007.DOI:10.1088/1361-6560/ad092a.
[17]
Cao CL, Li QL, Tong J, et al. Artificial intelligence in thyroid ultrasound[J]. Front Oncol, 2023,13:1060702.DOI:10.3389/fonc.2023.1060702.
[18]
Li Y, Liu Y, Xiao J, et al. Clinical value of artificial intelligence in thyroid ultrasound:a prospective study from the real world[J]. Eur Radiol, 2023, 33(7):4513-4523.DOI:10.1007/s00330-022-09378-y.
[19]
Merigó JM, Gil-Lafuente AM, Yager RR. An overview of fuzzy research with bibliometric indicators[J]. Appl Soft Comput, 2015, 27:420-433.DOI:10.1016/j.asoc.2014.10.035.
[20]
Van Eck N, Waltman L. Software survey:VOSviewer,a computer program for bibliometric mapping[J]. Scientometrics, 2010, 84(2):523-538.DOI:10.1007/s11192-009-0146-3
[21]
Chen C. CiteSpace Ⅱ:detecting and visualizing emerging trends and transient patterns in scientific literature[J]. J Am Soc Inf Sci Tec, 2006, 57(3):359-377.DOI:10.1002/asi.20317.
[22]
Haugen BR, Alexander EK, Bible KC, et al. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer:the american thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer[J]. Thyroid, 2016, 26(1):1-133.DOI:10.1089/thy.2015.0020.
[23]
Tessler FN, Middleton WD, Grant EG, et al. ACR thyroid imaging,reporting and data system(TI-RADS):white paper of the ACR TI-RADS committee[J]. J Am Coll Radiol, 2017, 14(5):587-595.DOI:10.1016/j.jacr.2017.01.046.
[24]
He K, Zhang X, Ren S, et al. Proceedings of the IEEE conference on computer vision and pattern recognition[C]. Las Vegas,NV, USA: IEEE Computer Society, 2016:770-778.DOI:10.1109/CVPR.2016.90.
[25]
Chi J, Walia E, Babyn P, et al. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network[J]. J Digit Imaging, 2017, 30(4):477-486.DOI:10.1007/s10278-017-9997-y.
[26]
Ronneberger O, Fischer P, Brox T. Medical image computing and computer-assisted intervention-MICCAI 2015:18th international conference[C]. Munich, Germany: Springer Verlag,2015:234-241.DOI:10.1007/978-3-319-24574-4_28.
[27]
Li X, Zhang S, Zhang Q, et al. Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images:a retrospective,multicohort,diagnostic study[J]. Lancet Oncol, 2019, 20(2):193-201.DOI:10.1016/S1470-2045(18)30762-9.
[28]
Ma J, Wu F, Zhu J, et al. A pre-trained convolutional neural network based method for thyroid nodule diagnosis[J]. Ultrasonics, 2017, 73:221-230.DOI:10.1016/j.ultras.2016.09.011.
[29]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks[J]. Commun Acm, 2017, 60(6):84-90.DOI:10.1145/3065386.
[30]
Shin JH, Baek JH, Chung J, et al. Ultrasonography diagnosis and imaging-based management of thyroid nodules:revised korean society of thyroid radiology consensus statement and recommendations[J]. Korean J Radiol, 2016, 17(3):370-395.DOI:10.3348/kjr.2016.17.3.370.
[31]
Kwak JY, Han KH, Yoon JH, et al. Thyroid imaging reporting and data system for US features of nodules:a step in establishing better stratification of cancer risk[J]. Radiology, 2011, 260(3):892-899.DOI:10.1148/radiol.11110206.
[32]
Acharya UR, Faust O, Sree SV, et al. ThyroScreen system:high resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform[J]. Comput Methods Programs Biomed, 2012, 107(2):233-241.DOI:10.1016/j.cmpb.2011.10.001.
[33]
Acharya UR, Swapna G, Sree SV, et al. A review on ultrasound-based thyroid cancer tissue characterization and automated classification[J]. Technol Cancer Res Treat, 2014, 13(4):289-301.DOI:10.7785/tcrt.2012.500381.
[34]
Chang Y, Paul AK, Kim N, et al. Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images:a comparison with radiologist-based assessments[J]. Med Phys, 2016, 43(1):554.DOI:10.1118/1.4939060.
[35]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv[Preprint], 2014,arXiv:1409.1556.DOI:10.48550/arxiv.1409.1556.
[36]
Liu T, Guo Q, Lian C, et al. Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks[J]. Med Image Anal, 2019,58:101555.DOI:10.1016/j.media.2019.101555.
[37]
Manh V, Jia X, Xue W, et al. An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss[J]. Comput Biol Med, 2024,171:108137.DOI:10.1016/j.compbiomed.2024.108137.
[38]
Šára R, Smutek D, Sucharda P, et al. Conference on artificial intelligence in medicine in Europe[C]. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001:339-348.DOI:10.1017/s1431927619000163.
[39]
Chen Y, Xiao X, He Q, et al. Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022:a bibliometric analysis[J]. Heliyon, 2024, 10(3):e25318.DOI:10.1016/j.heliyon.2024.e25318.
[40]
Lee J, Yoon JH, Lee E, et al. Immune response and mesenchymal transition of papillary thyroid carcinoma reflected in ultrasonography features assessed by radiologists and deep learning[J]. J Adv Res, 2024, 62:219-228.DOI:10.1016/j.jare.2023.09.043.
[41]
Ali T, Pathan S, Salvi M, et al. CAROTIDNet:a novel carotid symptomatic/asymptomatic plaque detection system using cnn-based tangent optimization algorithm in B-mode ultrasound images[J]. IEEE Access, 2024, 12:73970-73979.DOI:10.1109/ACCESS.2024.3404023.
[42]
Rho M, Chun SH, Lee E, et al. Diagnosis of thyroid micronodules on ultrasound using a deep convolutional neural network[J]. Sci Rep, 2023, 13(1):7231.DOI:10.1038/s41598-023-34459-3.
[43]
Zhu J, Zhang S, Yu R, et al. An efficient deep convolutional neural network model for visual localization and automatic diagnosis of thyroid nodules on ultrasound images[J]. Quant Imaging Med Surg, 2021, 11(4):1368-1380.DOI:10.21037/qims-20-538.
[44]
Gong H, Chen J, Chen G, et al. Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules[J]. Comput Biol Med, 2023,155:106389.DOI:10.1016/j.compbiomed.2022.106389.
[45]
Chen C, Liu Y, Yao J, et al. Deep learning approaches for differentiating thyroid nodules with calcification:a two-center study[J]. BMC Cancer, 2023, 23(1):1139.DOI:10.1186/s12885-023-11456-3.
[46]
Li X, Pang S, Zhang R, et al. Attransunet:an enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation[J]. Comput Biol Med, 2023,152:106365.DOI:10.1016/j.compbiomed.2022.106365.
[47]
Zhou H, Wang K, Tian J. Online transfer learning for differential diagnosis of benign and malignant thyroid nodules with ultrasound images[J]. IEEE Trans Biomed Eng, 2020, 67(10):2773-2780.DOI:10.1109/TBME.2020.2971065.
[48]
Xiang Z, Zhuo Q, Zhao C, et al. Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis[J]. Comput Biol Med, 2022,150:106164.DOI:10.1016/j.compbiomed.2022.106164.
[49]
Deng P, Han X, Wei X, et al. Automatic classification of thyroid nodules in ultrasound images using a multi-task attention network guided by clinical knowledge[J]. Comput Biol Med, 2022,150:106172.DOI:10.1016/j.compbiomed.2022.106172.
[50]
Ashton J, Morrison S, Erkanli A, et al. Assessment of the diagnostic performance of a commercially available artificial intelligence algorithm for risk stratification of thyroid nodules on ultrasound[J]. Thyroid, 2024, 34(11):1379-1388.DOI:10.1089/thy.2024.0410.
[51]
Wang SR, Zhu PS, Li J, et al. Study on diagnosing thyroid nodules of ACR TI-RADS 4-5 with multimodal ultrasound radiomics technology[J]. J Clin Ultrasound, 2024, 52(3):274-283.DOI:10.1002/jcu.23625.
[52]
Wang M, Chen C, Xu Z, et al. An interpretable two-branch bi-coordinate network based on multi-grained domain knowledge for classification of thyroid nodules in ultrasound images[J]. Med Image Anal, 2024,97:103255.DOI:10.1016/j.media.2024.103255.
[53]
Zhou S, Jia C, Shu G, et al. Recent advances in tengs collecting acoustic energy:from low-frequency sound to ultrasound[J]. Nano Energy, 2024:109951.DOI:10.1016/j.nanoen.2024.109951.
[54]
Lu WJ, Mao L, Li J, et al. Three-dimensional ultrasound-based radiomics nomogram for the prediction of extrathyroidal extension features in papillary thyroid cancer[J]. Front Oncol, 2023,13:1046951.DOI:10.3389/fonc.2023.1046951.
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