Publications by authors named "Camilla F Aglen"
Article Synopsis
- The study looked at how artificial intelligence (AI) could help radiologists read mammogram results better by testing a lot of past data from BreastScreen Norway.
- They tested 11 different ways to use AI and radiologists together and found that some methods could lower the number of exams radiologists need to check.
- The results showed that using AI could keep cancer detection rates high while making it easier for radiologists, with some methods reducing their workload by up to 90%!
Background Artificial intelligence (AI) has shown promising results for cancer detection with mammographic screening. However, evidence related to the use of AI in real screening settings remain sparse. Purpose To compare the performance of a commercially available AI system with routine, independent double reading with consensus as performed in a population-based screening program.
View Article and Find Full Text PDFBackground: Digital breast tomosynthesis (DBT) improves breast cancer (BC) detection compared to mammography, however, it is unknown whether this reduces (ICR) at follow-up.
Methods: Using (IPD) from DBT screening studies (identified via periodic literature searches July 2016 to November 2019) we performed an IPD meta-analysis. We estimated ICR for DBT-screened participants and the difference in pooled ICR for DBT and mammography-only screening, and compared interval BC characteristics.