Breast cancer affects one in eight women in the US. There are multiple factors involved in developing the disease, but one issue is dense breast tissue. Some 40 percent of US women have dense breast tissue, which alone increases the risk of breast cancer, and can make mammogram screening more difficult. Now, researchers from MIT and Massachusetts General Hospital (MGH) have developed an automated model that assesses dense breast tissue in mammograms as reliably as expert radiologists. Breast density assessments have traditionally relied on subjective human exams and calculations, but the deep-learning model -- trained on tens of thousands of digital mammograms -- is able to distinguish different types of breast tissue, from fatty to extremely dense, with 90 percent correlation to radiologists' diagnosis.