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. 2021 Jan;40(1):23-29.
doi: 10.14366/usg.20068. Epub 2020 Jul 3.

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Eun Ju Ha  1 , Jung Hwan Baek  2
Affiliations

Applications of machine learning and deep learning to thyroid imaging: where do we stand?

Eun Ju Ha et al. Ultrasonography. 2021 Jan.

Abstract

Ultrasonography (US) is the primary diagnostic tool used to assess the risk of malignancy and to inform decision-making regarding the use of fine-needle aspiration (FNA) and postFNA management in patients with thyroid nodules. However, since US image interpretation is operator-dependent and interobserver variability is moderate to substantial, unnecessary FNA and/or diagnostic surgery are common in practice. Artificial intelligence (AI)-based computeraided diagnosis (CAD) systems have been introduced to help with the accurate and consistent interpretation of US features, ultimately leading to a decrease in unnecessary FNA. This review provides a developmental overview of the AI-based CAD systems currently used for thyroid nodules and describes the future developmental directions of these systems for the personalized and optimized management of thyroid nodules.

Keywords: Artificial intelligence; Computer-aided diagnosis; Neoplasms; Thyroid.

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Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.. History of risk-stratification systems and the development of artificial intelligence (AI)-based computer-aided diagnosis (CAD) systems.
US, ultrasonography.
Fig. 2.
Fig. 2.. Ultrasound (US) image interpretation of a thyroid nodule using a commercialized computer-aided diagnosis (CAD) system (S-Detect Thyroid).
A. A solid hypoechoic nodule with suspicious US features is evident in the right thyroid gland. B. CAD software automatically calculates the mass contours and presents US features on the right side of the screen, and a possible diagnosis as a malignant nodule at the bottom. See the video clip at the following link: https://www.youtube.com/watch?v=Bb2NNpF3zpI.
See this image and copyright information in PMC

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