TY - JOUR 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 SP - 369 LP - 378 DO - 10.1136/neurintsurg-2020-017099 VL - 13 IS - 4 AU - Melissa Yeo AU - Bahman Tahayori AU - Hong Kuan Kok AU - Julian Maingard AU - Numan Kutaiba AU - Jeremy Russell AU - Vincent Thijs AU - Ashu Jhamb AU - Ronil V Chandra AU - Mark Brooks AU - Christen D. Barras AU - Hamed Asadi Y1 - 2021/04/01 UR - http://jnis.bmj.com/content/13/4/369.abstract N2 - 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. ER -