USG :: Ultrasonography

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm

Yiwen Deng, Qiao Zeng, Yu Zhao, Zhen Hu, Changmiao Zhan, Liangyun Guo, Binghuang Lai, Zhiping Huang, Zhiyong Fu, Chunquan Zhang

Journal of Ultrasound in Medicine.2025; 44(3): 567. CrossRef

Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases

Lina Yang, XinYuan Wang, Shixia Zhang, Kun Cao, Jianjun Yang

Interpretable Machine Learning to Predict the Malignancy Risk of Follicular Thyroid Neoplasms in Extremely Unbalanced Data: Retrospective Cohort Study and Literature Review

Rui Shan, Xin Li, Jing Chen, Zheng Chen, Yuan-Jia Cheng, Bo Han, Run-Ze Hu, Jiu-Ping Huang, Gui-Lan Kong, Hui Liu, Fang Mei, Shi-Bing Song, Bang-Kai Sun, Hui Tian, Yang Wang, Wu-Cai Xiao, Xiang-Yun Yao, Jing-Ming Ye, Bo Yu, Chun-Hui Yuan, Fan Zhang, Zheng

Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study

Ann C. Lin, Zelong Liu, Justine Lee, Gustavo Fernandez Ranvier, Aida Taye, Randall Owen, David S. Matteson, Denise Lee

A Novel Interpretable Radiomics Model to Distinguish Nodular Goiter From Malignant Thyroid Nodules

Hao Zhang, Yun-Feng Yang, Chao Yang, Yuan-Yuan Yang, Xin-Hong He, Chao Chen, Xue-Lin Song, Lei-Lei Ying, Ying Wang, Li-Chao Xu, Wen-Tao Li

Journal of Computer Assisted Tomography.2024; 48(2): 334. CrossRef

The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms

Weiwei Chen, Xuejun Ni, Cheng Qian, Lei Yang, Zheng Zhang, Mengdan Li, Fanlei Kong, Mengqin Huang, Maosheng He, Yifei Yin

Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI

Bin Song, Tingting Zheng, Hao Wang, Lang Tang, Xiaoli Xie, Qingyin Fu, Weiyan Liu, Pu-Yeh Wu, Mengsu Zeng

Journal of Imaging Informatics in Medicine.2024; 37(6): 2852. CrossRef

The Current Progress of Artificial Intelligence in Approach to Thyroid Nodules: A Narrative Review

Parsa Yazdanpanahi, Farnaz Atighi, Alireza Keshtkar, Reza Hamidi, Mohamadali Rezaeimanesh, Alireza Karimi, Arzhang Naseri, Mohammad Hossein Dabbaghmanesh

Models of ultrasonic radiomics and clinical characters for lymph node metastasis assessment in thyroid cancer: a retrospective study

Hui Zhu, Bing Yu, Yanyan Li, Yuhua Zhang, Juebin Jin, Yao Ai, Xiance Jin, Yan Yang

Comparison of S-Detect and thyroid imaging reporting and data system classifications in the diagnosis of cytologically indeterminate thyroid nodules

Ling Zhou, Lin-lin Zheng, Chuan-ju Zhang, Hong-fen Wei, Li-long Xu, Mu-rui Zhang, Qiang Li, Gao-fei He, Edem Prince Ghamor-Amegavi, Shi-yan Li

Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules

Lin-lin Zheng, Su-ya Ma, Ling Zhou, Cong Yu, Hai-shan Xu, Li-long Xu, Shi-yan Li

Chun-Li Cao, Qiao-Li Li, Jin Tong, Li-Nan Shi, Wen-Xiao Li, Ya Xu, Jing Cheng, Ting-Ting Du, Jun Li, Xin-Wu Cui

AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions

Yassine Habchi, Yassine Himeur, Hamza Kheddar, Abdelkrim Boukabou, Shadi Atalla, Ammar Chouchane, Abdelmalik Ouamane, Wathiq Mansoor

A Non-Invasive Follicular Thyroid Cancer Risk Prediction System Based on Deep Hybrid Multi-feature Fusion Network

Wu, PhD Yalin, Ge, MM Qiaoli, Yan, PhD Linyang, Sun, MD Desheng

ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY.2023; 7(4): 373. CrossRef

A hybrid optimization algorithm‐based feature selection for thyroid disease classifier with rough type‐2 fuzzy support vector machine

Vidhushavarshini Sureshkumar, Sathiyabhama Balasubramaniam, Vinayakumar Ravi, Ajay Arunachalam

Radiomic Detection of Malignancy within Thyroid Nodules Using Ultrasonography—A Systematic Review and Meta-Analysis

Eoin F. Cleere, Matthew G. Davey, Shane O’Neill, Mel Corbett, John P O’Donnell, Sean Hacking, Ivan J. Keogh, Aoife J. Lowery, Michael J. Kerin

Deep convolutional neural networks in thyroid disease detection: A multi-classification comparison by ultrasonography and computed tomography

Xinyu Zhang, Vincent CS. Lee, Jia Rong, James C. Lee, Feng Liu

Computer Methods and Programs in Biomedicine.2022; 220: 106823. CrossRef

Differentiate Thyroid Follicular Adenoma from Carcinoma with Combined Ultrasound Radiomics Features and Clinical Ultrasound Features

Bing Yu, Yanyan Li, Xiangle Yu, Yao Ai, Juebin Jin, Ji Zhang, YuHua Zhang, Hui Zhu, Congying Xie, Meixiao Shen, Yan Yang, Xiance Jin

Journal of Digital Imaging.2022; 35(5): 1362. CrossRef

Clinicoradiological Characteristics in the Differential Diagnosis of Follicular-Patterned Lesions of the Thyroid: A Multicenter Cohort Study

Jeong Hoon Lee, Eun Ju Ha, Da Hyun Lee, Miran Han, Jung Hyun Park, Ji-hoon Kim

Korean Journal of Radiology.2022; 23(7): 763. CrossRef

A comparison of artificial intelligence versus radiologists in the diagnosis of thyroid nodules using ultrasonography: a systematic review and meta-analysis

Pimrada Potipimpanon, Natamon Charakorn, Prakobkiat Hirunwiwatkul

European Archives of Oto-Rhino-Laryngology.2022; 279(11): 5363. CrossRef

A multi-channel deep convolutional neural network for multi-classifying thyroid diseases

Xinyu Zhang, Vincent C.S. Lee, Jia Rong, James C. Lee, Jiangning Song, Feng Liu

Computers in Biology and Medicine.2022; 148: 105961. CrossRef

The Value of Sonography in Distinguishing Follicular Thyroid Carcinoma from Adenoma

Wen Li, Qing Song, Yu Lan, Jie Li, Ying Zhang, Lin Yan, Yingying Li, Yan Zhang, Yukun Luo

Cancer Management and Research.2021; Volume 13: 3991. CrossRef