A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients
Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.
Authors
- Xinghao Wang (ORCID: https://orcid.org/0000-0001-5751-7383)
- Hongzan Sun (ORCID: https://orcid.org/0000-0002-4724-5828)
- Marcin Grzegorzek (ORCID: https://orcid.org/0000-0003-4877-8287)
- Zeyd Boukhers (ORCID: https://orcid.org/0000-0001-9778-9164)
- Tianming Du (ORCID: https://orcid.org/0000-0001-7671-6513)
- Chen Li (ORCID: https://orcid.org/0000-0003-0704-8763)
- Ning Xu (ORCID: https://orcid.org/0000-0001-7260-3490)
- Tao Jiang (ORCID: https://orcid.org/0009-0007-6524-5382)
- Yihao Sun
Institutions
- Liaoning Shihua University (CN)
- Capital Medical University (CN)
- German Research Centre for Artificial Intelligence (DE)
- Fraunhofer Institute for Applied Information Technology (DE)
- Beijing Friendship Hospital (CN)
- Chengdu University of Traditional Chinese Medicine (CN)
- China Medical University (CN)
- Northeastern University (CN)
- University of Lübeck (DE)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2024-08-12
- DOI
- https://doi.org/10.3389/fmed.2024.1444708
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chengdu Science and Technology Bureau