Cargando…
The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study
OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD). METHODS: A total of 563 chest CT scans of 380 patients (227/3...
Autores principales: | Xie, Qiuchen, Lu, Yiping, Xie, Xiancheng, Mei, Nan, Xiong, Yun, Li, Xuanxuan, Zhu, Yangyong, Xiao, Anling, Yin, Bo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769567/ https://www.ncbi.nlm.nih.gov/pubmed/33372243 http://dx.doi.org/10.1007/s00330-020-07553-7 |
Ejemplares similares
-
Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?
por: Jungmann, Florian, et al.
Publicado: (2021) -
Automated quantification of COVID-19 severity and progression using chest CT images
por: Pu, Jiantao, et al.
Publicado: (2020) -
Automated detection and segmentation of intracranial hemorrhage suspect hyperdensities in non-contrast-enhanced CT scans of acute stroke patients
por: Schmitt, N., et al.
Publicado: (2021) -
Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19
por: Feng, Zhichao, et al.
Publicado: (2021) -
A PET/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer
por: Xie, Yunming, et al.
Publicado: (2021)