These deep learning algorithms outperformed a panel of 11 pathologists

During a 2016 simulation exercise, researchers evaluated the ability of 32 different deep learning algorithms to detect lymph node metastases in patients with breast cancer. Each algorithm's performance was then compared to that of a panel of 11 pathologists with time constraint (WTC). Overall, the team found that seven of the algorithms outperformed the panel of pathologists, publishing an in-depth analysis in JAMA. "To our knowledge, this is the first study that shows that interpretation of pathology images can be performed by deep learning algorithms at an accuracy level that rivals human performance," wrote lead author Babak Ehteshami Bejnordi, MS, Radboud University Medical Center in Nijmegen, the Netherlands, and colleagues. The simulation took place during the Cancer Metastases in Lymph Nodes Challenge 2016 (CAMELYON16) in the Netherlands.