Value of Artificial Intelligence in Improving the Accuracy of Diagnosing TI-RADS Category 4 Nodules
M Lai, B Feng, J Yao, Y Wang, Q Pan, Y Chen… - Ultrasound in Medicine …, 2023 - Elsevier
Objective Considerable heterogeneity is observed in the malignancy rates of thyroid
nodules classified as category 4 according to the Thyroid Imaging Reporting and Data …
nodules classified as category 4 according to the Thyroid Imaging Reporting and Data …
Applying machine-learning models to differentiate benign and malignant thyroid nodules classified as C-TIRADS 4 based on 2D-ultrasound combined with five …
J Chen, YQ Zhang, T Zhu, Q Zhang, A Zhao… - Frontiers in …, 2024 - frontiersin.org
Objectives To apply machine learning to extract radiomics features from thyroid two-
dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) …
dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasound (CEUS) …
Smart scanning: automatic detection of superficially located lymph nodes using ultrasound–initial results
M Rink, J Künzel, C Stroszczynski… - RöFo-Fortschritte auf …, 2024 - thieme-connect.com
Over the last few years, there has been an increasing focus on integrating artificial
intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are …
intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are …
Integrating artificial intelligence (S‐Detect software) and contrast‐enhanced ultrasound for enhanced diagnosis of thyroid nodules: A comprehensive evaluation study
LL Zou, Q Zhang, Z Yao, Y He, J Zhou - Journal of Clinical Ultrasound - Wiley Online Library
Purpose This study aims to assess the diagnostic efficacy of Korean Thyroid imaging
reporting and data system (K‐TIRADS), S‐Detect software and contrast‐enhanced …
reporting and data system (K‐TIRADS), S‐Detect software and contrast‐enhanced …