Congress:
ECR25
Poster Number:
C-14717
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
A. Olesinski, R. Lederman, J. Sosna, L. Joskowicz; Jerusalem/IL
Disclosures:
Alon Olesinski:
Consultant: HighRAD
Richard Lederman:
Consultant: HighRAD
Jacob Sosna:
Consultant: HighRAD
Leo Joskowicz:
Consultant: HighRAD and Ezra
Keywords:
Artificial Intelligence, Lymph nodes, Oncology, CT, CAD, Computer Applications-Detection, diagnosis, Cancer
The enhanced 3D nnU-Net yielded a detection precision and recall (std) of 0.85 (0.26) and 0.89 (0.24) for LNs > 10mm and 0.73 (0.29) and 0.72 (0.28) for LNs 5-10mm. It significantly (p<0.01) improved the recall(std) by 6 (14)% and 15 (30)% with respect to the initial model, with similar precision. The mean short axis difference(std) was 4.6 (4.7)mm and 2.3(3.2)mm respectively.