Validation of a Deep Learning Tool for Detection of Incidental Vertebral Compression Fractures
CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.
Authors
- Daniel Chow (ORCID: https://orcid.org/0000-0002-2359-7394)
- Jacob Schlossman
- Angela Ayobi (ORCID: https://orcid.org/0000-0002-4351-6782)
- Yasmina Chaibi (ORCID: https://orcid.org/0000-0002-9734-172X)
- Bryan-Clement Tiu (ORCID: https://orcid.org/0000-0002-7101-099X)
- Jennifer E. Soun (ORCID: https://orcid.org/0000-0001-7385-8532)
- Michelle Dai (ORCID: https://orcid.org/0000-0002-3740-8347)
- Christophe Avare
- Peter C. Chang
- Charlotte Castineira
- Julie Kiewsky
Institutions
- Intel (United States) (US)
- University of California, Irvine (US)
- Touro University Nevada (US)
Publication Details
- Journal
- Journal of Computer Assisted Tomography
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1097/rct.0000000000001726
- Citations
- 2
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.35