Deep learning predicts cardiovascular disease risks from lung cancer screening low dose computed tomography
Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Here, the authors develop a deep learning model to perform this task, showing human-level performance.
Guardado en:
Autores principales: | Hanqing Chao, Hongming Shan, Fatemeh Homayounieh, Ramandeep Singh, Ruhani Doda Khera, Hengtao Guo, Timothy Su, Ge Wang, Mannudeep K. Kalra, Pingkun Yan |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/95de7da9fa1641daa7bb2a30bce80781 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deploying Clinical Process Improvement Strategies to Reduce Motion Artifacts and Expiratory Phase Scanning in Chest CT
por: Ruhani Doda Khera, et al.
Publicado: (2019) -
Low-dose x-ray tomography through a deep convolutional neural network
por: Xiaogang Yang, et al.
Publicado: (2018) -
Deep convolutional neural networks to predict cardiovascular risk from computed tomography
por: Roman Zeleznik, et al.
Publicado: (2021) -
Deep learning-based optical field screening for robust optical diffraction tomography
por: DongHun Ryu, et al.
Publicado: (2019) -
Deep learning models for screening of high myopia using optical coherence tomography
por: Kyung Jun Choi, et al.
Publicado: (2021)