Artificial Intelligence for Assessment of Digital Mammography Positioning Reveals Persistent Challenges
Artificial intelligence identified a similar distribution of unmet MPQ criteria in 2 health systems' daily work. Knowledge of current commonly unmet MPQ criteria can facilitate the improvement of mammography quality through tailored education strategies.
Authors
- Mohamed Abdolell (ORCID: https://orcid.org/0000-0003-1892-9961)
- Jennifer Payne (ORCID: https://orcid.org/0000-0001-9565-3685)
- Laurie R. Margolies (ORCID: https://orcid.org/0000-0002-7037-7570)
- Sian Iles (ORCID: https://orcid.org/0000-0003-3260-2770)
- Georgia Giakoumis Spear
Institutions
- Dalhousie University (CA)
- Mount Sinai Health System (US)
- NorthShore University HealthSystem (US)
- Health Sciences Centre (CA)
- Nova Scotia Health Authority (CA)
- University of Chicago (US)
- Victoria General Hospital (CA)
- Victoria General Hospital (CA)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Journal of Breast Imaging
- Published
- 2025-04-11
- DOI
- https://doi.org/10.1093/jbi/wbaf025
- Citations
- 1
- Primary Topic
- Digital Radiography and Breast Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.52