Toward non-invasive diagnosis of Bankart lesions with deep learning
This study demonstrates the feasibility of using DL to address the diagnostic challenges posed by subtle pathologies like Bankart lesions. Our models demonstrate potential to improve diagnostic confidence, reduce reliance on invasive imaging, and enhance accessibility to care.
Authors
- Sanjum S. Sethi (ORCID: https://orcid.org/0000-0002-3438-7034)
- Lewis L. Shi (ORCID: https://orcid.org/0000-0002-3533-8824)
- Nicholas H. Maassen (ORCID: https://orcid.org/0000-0002-1461-6469)
- Sai T. Reddy (ORCID: https://orcid.org/0000-0002-9177-0857)
- Mansi Sakarvadia (ORCID: https://orcid.org/0000-0002-0041-0160)
- Jordan Cook Serotte (ORCID: https://orcid.org/0009-0009-3975-441X)
- Darlington Nwaudo
Institutions
- University of Chicago (US)
Publication Details
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1117/12.3046251
- Primary Topic
- Sarcoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00