Update on Thyroid Ultrasound: a Narrative Review from Diagnostic Criteria to Artificial Intelligence Techniques

Overview

Journal Chin Med J (Engl)

Specialty General Medicine

Date 2019 Jul 27

PMID 31348028

Citations 7

Authors

Xiao-Wen Liang

Yong-Yi Cai

Jin-Sui Yu

Jian-Yi Liao

Zhi-Yi Chen

Affiliations

Soon will be listed here.

Abstract

Objective: Ultrasound imaging is well known to play an important role in the detection of thyroid disease, but the management of thyroid ultrasound remains inconsistent. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. This study reviewed the global guidelines of thyroid ultrasound and analyzed their common characteristics for basic clinical screening. Advances in the application of a combination of thyroid ultrasound and artificial intelligence (AI) were also presented.

Data Sources: An extensive search of the PubMed database was undertaken, focusing on research published after 2001 with keywords including thyroid ultrasound, guideline, AI, segmentation, image classification, and deep learning.

Study Selection: Several types of articles, including original studies and literature reviews, were identified and reviewed to summarize the importance of standardization and new technology in thyroid ultrasound diagnosis.

Results: Ultrasound has become an important diagnostic technique in thyroid nodules. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. In the standardization, since there are no global consensus exists, common characteristics such as a multi-feature diagnosis, the performance of lymph nodes, explicit indications of fine needle aspiration, and the diagnosis of special populations should be focused on. Besides, evidence suggests that AI technique has a good effect on the unavoidable limitations of traditional ultrasound, and the combination of diagnostic criteria and AI may lead to a great promotion in thyroid diagnosis.

Conclusion: Standardization and development of novel techniques are key factors to improving thyroid ultrasound, and both should be considered in normal clinical use.

Citing Articles

Study on diagnosis of thyroid nodules based on convolutional neural network.

Yin A, Lu Y, Xu F, Zhao Y, Sun Y, Huang M Radiologie (Heidelb). 2023; 63(Suppl 2):64-72.

PMID: 37074397 DOI: 10.1007/s00117-023-01137-4.

Development of a Machine Learning-Based Screening Method for Thyroid Nodules Classification by Solving the Imbalance Challenge in Thyroid Nodules Data.

Khodabandelu S, Ghaemian N, Khafri S, Ezoji M, Khaleghi S J Res Health Sci. 2022; 22(3):e00555.

PMID: 36511373 PMC: 10422153. DOI: 10.34172/jrhs.2022.90.

The Spectrum of Thyroid Nodules at Kinshasa University Hospital, Democratic Republic of Congo: A Cross-Sectional Study.

Bukasa J, Bayauli-Mwasa P, Mbunga B, Bangolo A, Kavula W, Mukaya J Int J Environ Res Public Health. 2022; 19(23).

PMID: 36498276 PMC: 9737877. DOI: 10.3390/ijerph192316203.

Ultrasound-based deep learning using the VGGNet model for the differentiation of benign and malignant thyroid nodules: A meta-analysis.

Zhu P, Zhang Y, Ren J, Li Q, Chen M, Sang T Front Oncol. 2022; 12:944859.

PMID: 36249056 PMC: 9554631. DOI: 10.3389/fonc.2022.944859.

A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules.

Liang X, Huang Y, Cai Y, Liao J, Chen Z Front Oncol. 2021; 11:611436.

PMID: 34692466 PMC: 8529148. DOI: 10.3389/fonc.2021.611436.

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