Gastrointest Endosc 2021 03 16;93(3):662-670. Epub 2020 Sep 16.
Research Service, VA Boston Healthcare System, Boston, MA; Department of Biomedical Engineering, Boston University College of Engineering, Boston, MA; Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, Boston, MA; Department of Medicine, Boston University School of Medicine, Boston, MA; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Background And Aims: Artificial intelligence (AI)-based computer-aided diagnostic (CADx) algorithms are a promising approach for real-time histology (RTH) of colonic polyps. Our aim is to present a novel in situ CADx approach that seeks to increase transparency and interpretability of results by generating an intuitive augmented visualization of the model's predicted histology over the polyp surface.
Methods: We developed a deep learning model using semantic segmentation to delineate polyp boundaries and a deep learning model to classify subregions within the segmented polyp. Read More