A Multisite Study of a Breast Density Deep Learning Model for Full-Field Digital Mammography and Synthetic Mammography.

Thomas P Matthews¹, Sadanand Singh¹, Brent Mombourquette¹, Jason Su¹, Meet P Shah¹, Stefano Pedemonte¹, Aaron Long¹, David Maffit¹, Jenny Gurney¹, Rodrigo Morales Hoil¹, Nikita Ghare¹, Douglas Smith¹, Stephen M Moore¹, Susan C Marks¹, Richard L Wahl¹

¹Whiterabbit AI, Inc, 3930 Freedom Circle, Suite 101, Santa Clara, CA 95054 (T.P.M., S.S., B.M., J.S., M.P.S., S.P., A.L., R.M.H., N.G., D.S.); Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (D.M., J.G., S.M.M., R.L.W.); and Peninsula Diagnostic Imaging, San Mateo, Calif (S.C.M.).

PubMed PMC DOI PDF

Abstract

PURPOSE: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic (SM) images derived from digital breast tomosynthesis examinations by using full-field digital mammographic (FFDM) images and limited SM data. MATERIALS AND METHODS: A DL model was trained to predict BI-RADS breast densi…

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