Congress:
ECR25
Poster Number:
C-24405
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
M. Wodrich, F. Sahlin, J. Karlsson, I. Arvidsson, K. Lang; Lund/SE
Disclosures:
Marisa Wodrich:
Nothing to disclose
Freja Sahlin:
Nothing to disclose
Jennie Karlsson:
Nothing to disclose
Ida Arvidsson:
Nothing to disclose
Kristina Lang:
Nothing to disclose
Keywords:
Artificial Intelligence, Breast, Oncology, Ultrasound, Comparative studies, Computer Applications-Detection, diagnosis, Screening, Cancer
Breast cancer diagnosis using high-end ultrasound is costly in terms of time and machines, and requires highly trained breast radiologists. The use of point-of-care ultrasound (POCUS) combined with artificial intelligence (AI) could be a cost-effective solution for limited-resource settings.
The following learning objectives have been identified:
- To test for non-inferiority of POCUS compared to standard breast ultrasound (BUS)
- To compare the performance of AI to expert radiologists in breast cancer detection on POCUS and standard breast ultrasound (BUS)
- To develop a proof of concept for combining AI and POCUS for breast cancer detection in limited-resource settings