The AI-based triaging system has the potential to significantly reduce radiologist workload, Verburg said. In the Netherlands alone, nearly 82,000 women may be eligible for biennial MRI breast screening based on breast density.
"The approach can first be used to assist radiologists to reduce overall reading time," Verburg said. "Consequently, more time could become available to focus on the really complex breast MRI examinations."
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The researchers plan to validate the model in other datasets and deploy it in subsequent screening rounds of the DENSE trial.
"Deep Learning for Automated Triaging of 4581 breast MRI Examinations from the DENSE Trial." Collaborating with Erik Verburg were Carla H. van Gils, Ph.D., Bas H.M. van der Velden, Ph.D., Marije F. Bakker, Ph.D., Ruud M. Pijnappel, M.D., Ph.D., Wouter B. Veldhuis, M.D., Ph.D., and Kenneth G. A. Gilhuijs, Ph.D.
Radiology is edited by David A. Bluemke, M.D., Ph.D., University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, and owned and published by the Radiological Society of North America, Inc.
RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois.
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