External validation of an algorithm to detect vertebral level mislabeling and autocontouring errors
A post-processing adjustment within the machine learning pipeline increased performance of vertebral body localization accuracy to acceptable levels for clinical use. External validation of machine learning and deep learning tools is essential to perform before deployment to different insitutions.
Authors
- Ajay Aggarwal (ORCID: https://orcid.org/0000-0001-9645-6659)
- Laurence E. Court (ORCID: https://orcid.org/0000-0002-3241-6145)
- Christoph Trauernicht (ORCID: https://orcid.org/0000-0002-0259-2148)
- Tina Patel (ORCID: https://orcid.org/0000-0002-2578-3393)
- Didier Duprez (ORCID: https://orcid.org/0000-0002-8533-8072)
- Tucker Netherton (ORCID: https://orcid.org/0000-0003-1583-7121)
- Gizem Cifter
Institutions
- University of London (GB)
- The University of Texas MD Anderson Cancer Center (US)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Stellenbosch University (ZA)
- Tygerberg Hospital (ZA)
- London School of Hygiene & Tropical Medicine (GB)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-02-26
- DOI
- https://doi.org/10.1016/j.phro.2025.100738
- Citations
- 1
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- Wellcome Trust
- National Institutes of Health
- University of Texas MD Anderson Cancer Center