Can AI Improve the Consistency of Breast Density Assessment by Radiologists?
In a recent video interview, Susan Holley, MD discussed key findings from a large retrospective longitudinal study, presented at the recent Radiological Society of North America (RSNA) conference, which found that an emerging artificial intelligence (AI) model was over 24 percent more consistent than radiologist assessment of breast density.
Artificial intelligence (AI) could play a key role in improving the consistency of breast density assessment, according to new research presented at the recent Radiological Society of North America (RSNA) conference.
In a retrospective, longitudinal study that examined the effectiveness of an emerging artificial intelligence (AI) model (WRDensity, Whiterabbit.ai ) for assessing breast density, researchers reviewed mammography data from over 61,000 patients who had three or more mammograms over a five-year period. They found the AI model was over 24 percent more consistent than radiologist assessment in assessing breast density via the Breast Imaging Reporting and Data System (BI-RADS).
In a recent video interview, Susan Holley, MD, a co-author of the study who presented the findings at the RSNA conference, said the AI model can help address the “innate variability” that can occur with radiologist assessment of breast density. As Dr. Holley pointed out, adjunctive AI assessment could be beneficial in risk stratification, determining the potential need for supplemental screening and documenting breast density assessment in radiology reports.
“With breast density being so important for risk assessment, supplemental screening and also now with a national breast density notification law that is looking like it’s going to actually happen in 2023, looking at variability and how we can get better at density assessment is even more timely and more critical,” noted Dr. Holley, a radiologist who is affiliated with UNC Health Care in Raleigh, N.C.
For more insights from Dr. Holley, watch the video below.
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