An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline...

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Detalles Bibliográficos
Autores principales: Kim, Chris K., Choi, Ji Whae, Jiao, Zhicheng, Wang, Dongcui, Wu, Jing, Yi, Thomas Y., Halsey, Kasey C., Eweje, Feyisope, Tran, Thi My Linh, Liu, Chang, Wang, Robin, Sollee, John, Hsieh, Celina, Chang, Ken, Yang, Fang-Xue, Singh, Ritambhara, Ou, Jie-Lin, Huang, Raymond Y., Feng, Cai, Feldman, Michael D., Liu, Tao, Gong, Ji Sheng, Lu, Shaolei, Eickhoff, Carsten, Feng, Xue, Kamel, Ihab, Sebro, Ronnie, Atalay, Michael K., Healey, Terrance, Fan, Yong, Liao, Wei-Hua, Wang, Jianxin, Bai, Harrison X.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760275/
https://www.ncbi.nlm.nih.gov/pubmed/35031687
http://dx.doi.org/10.1038/s41746-021-00546-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760275/
https://www.ncbi.nlm.nih.gov/pubmed/35031687
http://dx.doi.org/10.1038/s41746-021-00546-w

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