AI-Based Approach Reduces False Positives in Mammography

They built CNN models and used enhanced model training approaches to investigate six classification scenarios that would help distinguish images of benign, malignant, and recalled-benign mammograms. Upon combining the datasets from FFDM and DDSM, the area under the curve (AUC) to distinguish benign, malignant, and recalled-benign images ranged from 0.76 to 0.91. The higher the AUC, the better the performance, with a maximum of 1, according to 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.

"We showed that there are imaging features unique to recalled-benign images that deep learning can identify and potentially help radiologists in making better decisions on whether a patient should be recalled or is more likely a false recall," said Wu. "Based on the consistent ability of our algorithm to discriminate all categories of mammography images, our findings indicate that there are indeed some distinguishing features/characteristics unique to images that are unnecessarily recalled. Our AI models can augment radiologists in reading these images and ultimately benefit patients by helping reduce unnecessary recalls."

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