Cargando…
Assessing Detection Accuracy of Computerized Sonographic Features and Computer-Assisted Reading Performance in Differentiating Thyroid Cancers
For ultrasound imaging of thyroid nodules, medical guidelines are all based on findings of sonographic features to provide clinicians management recommendations. Due to the recent development of artificial intelligence and machine learning (AI/ML) technologies, there have been computer-assisted dete...
Autores principales: | Tai, Hao-Chih, Chen, Kuen-Yuan, Wu, Ming-Hsun, Chang, King-Jen, Chen, Chiung-Nien, Chen, Argon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313277/ https://www.ncbi.nlm.nih.gov/pubmed/35884818 http://dx.doi.org/10.3390/biomedicines10071513 |
Ejemplares similares
-
Risk Stratification in Patients With Follicular Neoplasm on Cytology: Use of Quantitative Characteristics and Sonographic Patterns
por: Wu, Ming-Hsun, et al.
Publicado: (2021) -
Multi-Reader Multi-Case Study for Performance Evaluation of High-Risk Thyroid Ultrasound with Computer-Aided Detection
por: Wu, Ming-Hsun, et al.
Publicado: (2020) -
Quantitative analysis of echogenicity for patients with thyroid nodules
por: Wu, Ming-Hsun, et al.
Publicado: (2016) -
Computerized Cytological Features for Papillary Thyroid Cancer Diagnosis—Preliminary Report
por: Shih, Shyang-Rong, et al.
Publicado: (2019) -
New sonographic feature (C‐sign) to improve the prenatal accuracy of jejunal atresia
por: Chen, Dan, et al.
Publicado: (2021)