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Abstract #0150

Deep Learning for Triage of Normal Breast MRI Exams to an Abbreviated Interpretation Worklist

Arka Bhowmik1, Natasha Monga1, Kristin Belen1, Danny Martinez1, Elizabeth J. Sutton1, Katja Pinker-Domenig1, and Sarah Eskreis-Winkler1
1Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States


In this abstract, we develop and evaluate a deep learning model to identify completely normal screening breast MRIs for triage to an abbreviated interpretation worklist, a workflow that misses no cancers and markedly reduces radiologist interpretation times. In our held out test set, the algorithm triaged 20% of all screening exams to the abbreviated worklist and 80% to full interpretation worklist for radiologist without missing any cancer exams (100% sensitivity), which reduced the total projected reading time for exams from 148 hours to 119 hours.

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