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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Korean Society of Ultrasound in Medicine
2021-01-01
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| Series: | Ultrasonography |
| Subjects: | |
| Online Access: | http://www.e-ultrasonography.org/upload/usg-20068.pdf |
| Summary: | 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. |
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| ISSN: | 2288-5919 2288-5943 |