Bottom Line: An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
Journal in Which the Study was Published: Clinical Cancer Research, a journal of the American Association for Cancer Research.
Author: Shandong Wu, PhD, assistant professor of radiology, biomedical informatics, bioengineering, intelligent systems, and clinical and translational science, and director of the Intelligent Computing for Clinical Imaging lab in the Department of Radiology at the University of Pittsburgh, Pennsylvania
Background: “In order to catch breast cancer early and help reduce mortality, mammography is an important screening exam; however, it currently suffers from a high false recall rate,” said Wu. “These false recalls result in undue psychological stress for patients and a substantial increase in clinical workload and medical costs. Therefore, research on possible means to reduce false recalls in screening mammography is an important topic to investigate.”
How the Study Was Conducted: Wu and colleagues studied whether a technique in artificial intelligence called deep learning could be applied to analyze a large set of mammograms in order to distinguish images from
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