Congress:
ECR24
Poster Number:
C-14978
Type:
EPOS Radiologist (scientific)
DOI:
10.26044/ecr2024/C-14978
Authorblock:
K. Geißler1, N. Uchiyama2, R. Grimm3, T. Suzuki4, U. Fischer5, D. H. M. Szolar6, H. Meine1, H. Von Busch3, R. M. Murakami4; 1Bremen/DE, 2TOKYO/JP, 3Erlangen/DE, 4Tokyo/JP, 5Goettingen/DE, 6Graz/AT
Disclosures:
Kai Geißler:
Nothing to disclose
Nachiko Uchiyama:
Nothing to disclose
Robert Grimm:
Employee: Siemens Healthcare GmbH
Takashi Suzuki:
Employee: Siemens Healthcare GmbH
Uwe Fischer:
Nothing to disclose
Dieter H. M. Szolar:
Nothing to disclose
Hans Meine:
Nothing to disclose
Heinrich Von Busch:
Employee: Siemens Healthcare GmbH
Ryusuke Medical Murakami:
Nothing to disclose
Keywords:
Artificial Intelligence, Breast, MR, Computer Applications-Detection, diagnosis, Segmentation, Cancer
The AI-based lesion detection shows high sensitivity with an acceptable level of false positives, potentially supporting the radiologist’s workflow. By adjusting the cut-off value, false positives can be reduced but sensitivity also decreases. Thus, different operating points may be appropriate depending on the expected prevalence of cancer in the studied cohort.