Congress:
ECR24
Poster Number:
C-21633
Type:
EPOS Radiologist (scientific)
DOI:
10.26044/ecr2024/C-21633
Authorblock:
C. Salvatore1, M. Interlenghi1, E. Schiavon1, A. Lad1, D. Fazzini1, M. Alì1, S. Papa1, F. Sardanelli2, I. Castiglioni3; 1Milan/IT, 2San Donato Milanese, Milan/IT, 3Milano/IT
Disclosures:
Christian Salvatore:
Shareholder: DeepTrace Technologies S.R.L., Milan, Italy CEO: DeepTrace Technologies S.R.L., Milan, Italy
Matteo Interlenghi:
Shareholder: DeepTrace Technologies S.R.L., Milan, Italy Employee: DeepTrace Technologies S.R.L., Milan, Italy
Elia Schiavon:
Employee: DeepTrace Technologies S.R.L., Milan, Italy
Akash Lad:
Nothing to disclose
Deborah Fazzini:
Nothing to disclose
Marco Alì:
Other: Bracco Imaging
Sergio Papa:
Nothing to disclose
Francesco Sardanelli:
Nothing to disclose
Isabella Castiglioni:
Shareholder: DeepTrace Technologies S.R.L., Milan, Italy
Keywords:
Breast, Mammography, Computer Applications-General, Cancer
Our results show that a real-world use of a PACS-integrated deep-learning system to classify breast density on digital mammography or on synthetic 2D-views from digital breast tomosynthesis is feasible and reliable. Image artifacts associated to patient's breast incorrect positioning may lead to misclassification errors, with low occurrence rates.