# A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-fully-automatic-deep-learning-system-for-covid-19-diagnostic-and-prognostic/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Shuo Wang,Yunfei Zha,Weimin Li,Qingxia Wu,Xiaohu Li,Meng Niu,Meiyun Wang,Xiaoming Qiu,Hongjun Li,Yu He,Wei Gong,Yan Bai,Li Li,Yongbei Zhu,Liusu Wang,Jie Tian |
| Citations | 499 |
| DOI | 10.1183/13993003.00775-2020 |
| Fields | Medicine |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://erj.ersjournals.com/content/erj/56/2/2000775.full.pdf |
| OpenAlex ID | https://openalex.org/W3027764902 |
| PMID | 32444412 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Weimin Li](https://scholariq.org/researchers/weimin-li/)

## Paper primary topic

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)

## Paper topics

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [COVID-19 Clinical Research Studies](https://scholariq.org/topics/covid-19-clinical-research-studies/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.