Fully Automated Quantification of Functional Small Airway Disease at Inspiratory and Expiratory Chest CT Using Deep Learning
A deep learning–based fully automated tool for quantifying functional small airway disease at paired inspiratory and expiratory chest CT showed performance similar to that of conventional semimanual assessment, supporting improved clinical workflow efficiency.
Authors
- Maurice Pradella (ORCID: https://orcid.org/0000-0003-2449-7835)
- J Bremerich
- K Hostettler
- Stefan Gherca (ORCID: https://orcid.org/0009-0007-9134-0623)
- Shan Yang (ORCID: https://orcid.org/0000-0002-5209-0576)
- J Halter
- David J. Winkel (ORCID: https://orcid.org/0000-0001-7051-8022)
- Daiana Stolz (ORCID: https://orcid.org/0000-0003-0099-882X)
Institutions
- University of Freiburg (DE)
- University of Basel (CH)
- University Hospital of Basel (CH)
- Hospital Base (CL)
Publication Details
- Journal
- Radiology Cardiothoracic Imaging
- Published
- 2026-05-28
- DOI
- https://doi.org/10.1148/ryct.250215
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00