Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram
The automated BAC AI score shows a very strong correlation with manual BAC scoring in this external validation cohort. The automated BAC AI score may be a useful tool to promote the integration of BAC into mammography reports and to improve awareness of a woman's cardiovascular risk status.
Authors
- Laetitia Saccenti (ORCID: https://orcid.org/0000-0002-9765-0039)
- Alain Luciani (ORCID: https://orcid.org/0000-0003-0331-9474)
- Refaat Nouri (ORCID: https://orcid.org/0000-0002-7836-7086)
- Lina Bejjani
- Vania Tacher (ORCID: https://orcid.org/0000-0002-7162-5425)
- Lise Minssen (ORCID: https://orcid.org/0000-0001-5953-9711)
- Hicham Kobeiter
- An Vo Quang (ORCID: https://orcid.org/0000-0001-9633-9858)
- Jean François Deux
- Thu Ha Dao
- Haifa Remili (ORCID: https://orcid.org/0000-0001-6669-8278)
- Bilel Ben Jedida
Institutions
- Inserm (FR)
- Université Paris-Est Créteil (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- University Hospital of Geneva (CH)
- Hôpitaux Universitaires Henri-Mondor (FR)
Publication Details
- Journal
- Diagnostic and Interventional Imaging
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1016/j.diii.2024.10.001
- Citations
- 8
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.48