Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.

Objective: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented.

Methods: This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022.

Potential Impact of an Artificial Intelligence-based Mammography Triage Algorithm on Performance and Workload in a Population-based Screening Sample.

Objective: To evaluate potential screening mammography performance and workload impact using a commercial artificial intelligence (AI)-based triage device in a population-based screening sample.

Methods: In this retrospective study, a sample of 2129 women who underwent screening mammograms were evaluated. The performance of a commercial AI-based triage device was compared with radiologists' reports, actual outcomes, and national benchmarks using commonly used mammography metrics.

Multicenter, Multivendor Validation of an FDA-approved Algorithm for Mammography Triage.

Objective: Artificial intelligence (AI)-based triage algorithms may improve cancer detection and expedite radiologist workflow. To this end, the performance of a commercial AI-based triage algorithm on screening mammograms was evaluated across breast densities and lesion types.

Methods: This retrospective, IRB-exempt, multicenter, multivendor study examined 1255 screening 4-view mammograms (400 positive and 855 negative studies).