Machine Learning For Use In Identifying Negative Mammograms

Making a difference: How AI can improve radiologist workflow

By Michael Walter

Machine learning can help reduce a radiologist’s workload by identifying negative mammograms that do not need to be interpreted, according to new findings published in the Journal of the American College of Radiology.

“As examination volumes and time of interpretation increase with newer screening technologies such as digital breast tomosynthesis, radiologists will be under increasing pressure to deliver a timely service,” wrote lead author Trent Kyono, department of computer science at the University of California Los Angeles, and colleagues. “Because the large majority of mammograms a radiologist examines are negative, machine learning methods that triage a subset of examinations as negative with extremely high accuracy and refer the rest to a breast imager could significantly reduce the daily interpretive workload of radiologists, freeing up time to focus on more suspicious examinations and diagnostic workups.”