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

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

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 post-FNA management in patients with thyroid nodules. However, since US image interpretation is operator-dependent and interob...

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Main Authors: Eun Ju Ha, Jung Hwan Baek
Format: Article
Language:English
Published: Korean Society of Ultrasound in Medicine 2021-01-01
Series:Ultrasonography
Subjects:
thyroid
neoplasms
artificial intelligence
computer-aided diagnosis
Online Access:http://www.e-ultrasonography.org/upload/usg-20068.pdf
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spelling doaj-de9708f1ff754ceb90222bc399fb7d682020-12-23T23:12:21ZengKorean Society of Ultrasound in MedicineUltrasonography2288-59192288-59432021-01-01401232910.14366/usg.200681127Applications of machine learning and deep learning to thyroid imaging: where do we stand?Eun Ju Ha0Jung Hwan Baek1 Department of Radiology, Ajou University School of Medicine, Suwon, Korea Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, KoreaUltrasonography (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 post-FNA 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 computer-aided 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.http://www.e-ultrasonography.org/upload/usg-20068.pdfthyroidneoplasmsartificial intelligencecomputer-aided diagnosis
collection DOAJ
language English
format Article
sources DOAJ
author Eun Ju Ha
Jung Hwan Baek
spellingShingle Eun Ju Ha
Jung Hwan Baek
Applications of machine learning and deep learning to thyroid imaging: where do we stand?
Ultrasonography
thyroid
neoplasms
artificial intelligence
computer-aided diagnosis
author_facet Eun Ju Ha
Jung Hwan Baek
author_sort Eun Ju Ha
title Applications of machine learning and deep learning to thyroid imaging: where do we stand?
title_short Applications of machine learning and deep learning to thyroid imaging: where do we stand?
title_full Applications of machine learning and deep learning to thyroid imaging: where do we stand?
title_fullStr Applications of machine learning and deep learning to thyroid imaging: where do we stand?
title_full_unstemmed Applications of machine learning and deep learning to thyroid imaging: where do we stand?
title_sort applications of machine learning and deep learning to thyroid imaging: where do we stand?
publisher Korean Society of Ultrasound in Medicine
series Ultrasonography
issn 2288-5919
2288-5943
publishDate 2021-01-01
description 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 post-FNA 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 computer-aided 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.
topic thyroid
neoplasms
artificial intelligence
computer-aided diagnosis
url http://www.e-ultrasonography.org/upload/usg-20068.pdf
work_keys_str_mv AT eunjuha applicationsofmachinelearninganddeeplearningtothyroidimagingwheredowestand
AT junghwanbaek applicationsofmachinelearninganddeeplearningtothyroidimagingwheredowestand
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