ORNL researchers use AI to improve mammogram interpretation

OAK RIDGE, Tenn., June 19, 2018 – In an effort to reduce errors in the analyses of diagnostic images by health professionals, a team of researchers from the Department of Energy's Oak Ridge National Laboratory has improved understanding of the cognitive processes involved in image interpretation. The work, published in the Journal of Medical Imaging, has potential to improve health outcomes for the hundreds of thousands of American women affected by breast cancer each year. Breast cancer is the second leading cause of death in women and early detection is critical for effective treatment. Catching the disease early requires an accurate interpretation of a patient's mammogram; conversely, a radiologist's misinterpretation of a mammogram can have enormous consequences for a patient's future. The ORNL-led team, which included Gina Tourassi, Hong-Jun Yoon and Folami Alamudun, as well as Paige Paulus of the University of Tennessee's Department of Mechanical, Aerospace, and Biomedical Engineering, found that analyses of mammograms by radiologists were significantly influenced by context bias, or the radiologist's previous diagnostic experiences.