Deep learning applications in visual data for benign and malignant hematological conditions: a systematic review and visual glossary

Abstract

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have been previously explored. Similarly, DL research and applications within hematology are rapidly emerging, yet still largely within its infancy. Given the exponential rise of DL research for hematological conditions, it is essential for the practicing hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles as well as a systematic review of published investigations within malignant and non-malignant hematological conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.