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Title: Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.
Austin Authors: Yeo, Melissa;Tahayori, Bahman;Kok, Hong Kuan;Maingard, Julian;Kutaiba, Numan ;Thijs, Vincent N ;Jhamb, Ashu;Chandra, Ronil V;Brooks, Mark;Barras, Christen D;Asadi, Hamed ;Russell, Jeremy H 
Affiliation: Melbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia
Department of Radiology, Northern Health, Epping, Victoria, Australia
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
IBM Research Australia, Melbourne, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health
Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia
South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
Department of Radiology, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia
School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia
Issue Date: Apr-2021
Date: 2021
Publication information: Journal of Neurointerventional Surgery 2021; 13(4): 369-378
Abstract: 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.
DOI: 10.1136/neurintsurg-2020-017099
ORCID: 0000-0001-5568-7303
Journal: Journal of Neurointerventional Surgery
PubMed URL: 33479036
Type: Journal Article
Subjects: CT
artificial intelligence
Appears in Collections:Journal articles

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