While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist: advocates argue for the ability to save lives, (women aged 60 to 69 had a 33 percent lower risk of dying compared to those who didn't get mammograms), and another camp argues about costly and potentially traumatic false positives (a meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography). Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch all: women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning and Health to ask: Can we use machine learning to provide personalized screening? Out of this came Tempo, a technology for creating risk-based screening guidelines.
Jan-22-2022, 11:15:08 GMT