A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.

Stefano Pedemonte¹, Trevor Tsue¹, Brent Mombourquette¹, Yen Nhi Truong Vu¹, Thomas Matthews¹, Rodrigo Morales Hoil¹, Meet Shah¹, Nikita Ghare¹, Naomi Zingman-Daniels¹, Susan Holley¹, Catherine M Appleton¹, Jason Su¹, Richard L Wahl¹

¹From Whiterabbit.ai, 3930 Freedom Cir, Santa Clara, CA 95054 (S.P., T.T., B.M., Y.N.T.V., T.M., R.M.H., M.S., N.G., N.Z.D., J.S.); Onsite Women's Health, Westfield, Mass (S.H.); SSM Health, St Louis, Mo (C.M.A.); and Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Mo (R.L.W.).

PubMed PMC DOI PDF

Abstract

Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping dat…

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