An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules
Published 2022 View Full Article
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Title
An Artificial Intelligence Model Based on ACR TI-RADS Characteristics for US Diagnosis of Thyroid Nodules
Authors
Keywords
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Journal
RADIOLOGY
Volume 303, Issue 3, Pages 613-619
Publisher
Radiological Society of North America (RSNA)
Online
2022-03-22
DOI
10.1148/radiol.211455
References
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Related references
Note: Only part of the references are listed.- Diagnostic performance of US-based FNAB criteria of the 2020 Chinese guideline for malignant thyroid nodules: comparison with the 2017 American College of Radiology guideline, the 2015 American Thyroid Association guideline, and the 2016 Korean Thyroid Association guideline
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- Inter- and Intraobserver Agreement in the Assessment of Thyroid Nodule Ultrasound Features and Classification Systems: A Blinded Multicenter Study
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- Differential Diagnosis of Benign and Malignant Thyroid Nodules Using Deep Learning Radiomics of Thyroid Ultrasound Images
- (2020) Hui Zhou et al. EUROPEAN JOURNAL OF RADIOLOGY
- Global trends in thyroid cancer incidence and the impact of overdiagnosis
- (2020) Mengmeng Li et al. Lancet Diabetes & Endocrinology
- The Added Value of Operator's Judgement in Thyroid Nodule Ultrasound Classification Arising From Histologically Based Comparison of Different Risk Stratification Systems
- (2020) Bruno Madeo et al. Frontiers in Endocrinology
- Diagnostic Performance of Practice Guidelines for Thyroid Nodules: Thyroid Nodule Size versus Biopsy Rates
- (2019) Su Min Ha et al. RADIOLOGY
- Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks
- (2019) Tianjiao Liu et al. MEDICAL IMAGE ANALYSIS
- Sonographic Criteria Predictive of Malignant Thyroid Nodules
- (2018) Carlos Miguel Oliveira et al. ACADEMIC RADIOLOGY
- Interobserver Variability of Sonographic Features Used in the American College of Radiology Thyroid Imaging Reporting and Data System
- (2018) Jenny K. Hoang et al. AMERICAN JOURNAL OF ROENTGENOLOGY
- The Diagnosis and Management of Thyroid Nodules
- (2018) Cosimo Durante et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators
- (2018) Eun Young Jeong et al. EUROPEAN RADIOLOGY
- Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study
- (2018) Xiangchun Li et al. LANCET ONCOLOGY
- Intraobserver and Interobserver Variability in Ultrasound Measurements of Thyroid Nodules
- (2017) Hyung Jin Lee et al. JOURNAL OF ULTRASOUND IN MEDICINE
- Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
- (2017) Gaël Varoquaux et al. NEUROIMAGE
- A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment
- (2017) Young Jun Choi et al. THYROID
- The 2017 Bethesda System for Reporting Thyroid Cytopathology
- (2017) Edmund S. Cibas et al. THYROID
- ACR Thyroid Imaging, Reporting and Data System (TI-RADS): White Paper of the ACR TI-RADS Committee
- (2017) Franklin N. Tessler et al. Journal of the American College of Radiology
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