Semi-quantitative software evaluation of COVID-19 CT examinations—correlation with clinical parameters
COVID-19 pneumonia may be semi-quantified using an artificial intelligence (AI)-based software approach. Quantitative methods could provide precise information on the volume of opacities and may allow detecting connections to patient therapy, including the need for intensive care.
Authors
- Nico Sollmann (ORCID: https://orcid.org/0000-0002-8120-2223)
- Bettina Jungwirth (ORCID: https://orcid.org/0000-0001-9749-7460)
- Beate Grüner (ORCID: https://orcid.org/0000-0001-6669-3741)
- Christopher Kloth (ORCID: https://orcid.org/0000-0001-9137-3678)
- Thomas Stamminger (ORCID: https://orcid.org/0000-0001-9878-3119)
- S. Schmidt (ORCID: https://orcid.org/0000-0003-2896-3290)
- Meinrad Beer
- F. C. Flack (ORCID: https://orcid.org/0000-0002-2346-6781)
- Daniel Vogele (ORCID: https://orcid.org/0000-0002-6421-1400)
- Steffen Stenger (ORCID: https://orcid.org/0000-0001-5471-0807)
- Susanne Martina Büttner
- N Egenrieder
- Lynn Peters (ORCID: https://orcid.org/0000-0003-4882-6816)
- Daniel Wolf (ORCID: https://orcid.org/0000-0002-8584-5189)
- Nicoleta Trif
Institutions
- Universität Ulm (DE)
- TUM Klinikum (DE)
- Institute of Medical Microbiology and Hygiene (DE)
- Siemens Healthineers (Germany) (DE)
- University Hospital Ulm (DE)
Publication Details
- Journal
- Journal of Thoracic Disease
- Published
- 2025-10-01
- DOI
- https://doi.org/10.21037/jtd-2025-655
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00