Ultrasonography.
2021 Jan;40(1):23-29. 10.14366/usg.20068.
Applications of machine learning and deep learning to thyroid imaging: where do we stand?
- Affiliations
-
- 1Department of Radiology, Ajou University School of Medicine, Suwon, Korea
- 2Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- KMID: 2511608
- DOI: http://doi.org/10.14366/usg.20068
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.
- Full Text Links
-
- Actions
-
Cited
- CITED
-
- Close
- Share
-
- Similar articles
-
- Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions
- Current status of deep learning applications in abdominal ultrasonography
- Application of Machine Learning in Rhinology: A State of the Art Review
- Applications of Machine Learning Using Electronic Medical Records in Spine Surgery
- Deep Learning in MR Motion Correction:a Brief Review and a New Motion Simulation Tool (view2Dmotion)
