Severity assessment of COVID-19 disease: radiological visual score versus automated quantitative CT parameters using a pneumonia analysis algorithm
Visual and AI-based CT assessments showed high concordance and similar accuracy, especially in patients with >25% lung involvement. This study demonstrates the utility of AI-based algorithms to improve the diagnostic efficiency and the reliability, highlighting their value in routine COVID-19 pneumonia evaluation and management.
Authors
- Antonella Meloni (ORCID: https://orcid.org/0000-0002-2284-8585)
- Erasmo A. Passaro (ORCID: https://orcid.org/0009-0009-4575-2007)
- Filippo Cademartiri (ORCID: https://orcid.org/0000-0002-0579-3279)
- Sergio Berti
- Eduardo Bossone (ORCID: https://orcid.org/0000-0003-2769-9950)
- Erica Maffei (ORCID: https://orcid.org/0000-0002-0388-4433)
- Ludovico La Grutta (ORCID: https://orcid.org/0000-0002-3557-0084)
- Marco Aiello (ORCID: https://orcid.org/0000-0002-3676-0664)
- Bruna Punzo (ORCID: https://orcid.org/0000-0003-0915-4682)
- Carlo Cavaliere (ORCID: https://orcid.org/0000-0002-3297-2213)
- Monica Franzese (ORCID: https://orcid.org/0000-0002-6490-7694)
- Cesare Mantini (ORCID: https://orcid.org/0000-0001-6429-8836)
- Andrea Colacino (ORCID: https://orcid.org/0000-0001-5740-9497)
- Simona Celi
- Dario Baldi (ORCID: https://orcid.org/0000-0001-7464-4499)
- Luca Saba
Institutions
- University of Cagliari (IT)
- University of Chieti-Pescara (IT)
- Federico II University Hospital (IT)
- Fondazione Toscana Gabriele Monasterio (IT)
- University of Naples Federico II (IT)
- University of Palermo (IT)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-09-09
- DOI
- https://doi.org/10.3389/fmed.2025.1606771
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute