Development and validation of a deep learning-based algorithm for quantifying bronchiolitis obliterans in paediatric computed tomography
This study presents a 3D nnU-Net-based deep learning algorithm for robust quantification of BO on paediatric CT, providing reproducible measurements of LARs.
Authors
- Ji Young Ha (ORCID: https://orcid.org/0000-0001-5769-3045)
- Yeon Jin Cho (ORCID: https://orcid.org/0000-0001-9820-3030)
- Jinwoo Hwang (ORCID: https://orcid.org/0000-0002-5285-1128)
- Young Hun Choi (ORCID: https://orcid.org/0000-0002-1842-9062)
- Dong In Suh (ORCID: https://orcid.org/0000-0002-7817-8728)
- Jae Won Choi (ORCID: https://orcid.org/0000-0002-5937-7238)
- Jung‐Eun Cheon (ORCID: https://orcid.org/0000-0003-1479-2064)
- Seunghyun Lee (ORCID: https://orcid.org/0000-0003-1858-0640)
- C. van Rhee
Institutions
- Seoul National University (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- Pusan National University Hospital (KR)
- Pusan National University Yangsan Hospital (KR)
- Pusan National University (KR)
Publication Details
- Journal
- British Journal of Radiology
- Published
- 2025-10-29
- DOI
- https://doi.org/10.1093/bjr/tqaf268
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00