RT Journal Article SR Electronic T1 Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging JF Journal of NeuroInterventional Surgery JO J NeuroIntervent Surg FD BMJ Publishing Group Ltd. SP 369 OP 378 DO 10.1136/neurintsurg-2020-017099 VO 13 IS 4 A1 Melissa Yeo A1 Bahman Tahayori A1 Hong Kuan Kok A1 Julian Maingard A1 Numan Kutaiba A1 Jeremy Russell A1 Vincent Thijs A1 Ashu Jhamb A1 Ronil V Chandra A1 Mark Brooks A1 Christen D. Barras A1 Hamed Asadi YR 2021 UL http://jnis.bmj.com/content/13/4/369.abstract AB Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.