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dc.contributor.authorYeo, Melissa-
dc.contributor.authorTahayori, Bahman-
dc.contributor.authorKok, Hong Kuan-
dc.contributor.authorMaingard, Julian-
dc.contributor.authorKutaiba, Numan-
dc.contributor.authorThijs, Vincent N-
dc.contributor.authorJhamb, Ashu-
dc.contributor.authorChandra, Ronil V-
dc.contributor.authorBrooks, Mark-
dc.contributor.authorBarras, Christen D-
dc.contributor.authorAsadi, Hamed-
dc.contributor.authorRussell, Jeremy H-
dc.identifier.citationJournal of Neurointerventional Surgery 2021; 13(4): 369-378en
dc.description.abstractArtificial 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.en
dc.subjectartificial intelligenceen
dc.titleReview of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.en
dc.typeJournal Articleen
dc.identifier.journaltitleJournal of Neurointerventional Surgeryen
dc.identifier.affiliationMelbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australiaen
dc.identifier.affiliationDepartment of Radiology, Northern Health, Epping, Victoria, Australiaen
dc.identifier.affiliationDepartment of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australiaen
dc.identifier.affiliationIBM Research Australia, Melbourne, Victoria, Australiaen
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Healthen
dc.identifier.affiliationFaculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australiaen
dc.identifier.affiliationSchool of Medicine, The University of Adelaide, Adelaide, South Australia, Australiaen
dc.identifier.affiliationSouth Australian Health and Medical Research Institute, Adelaide, South Australia, Australiaen
dc.identifier.affiliationInterventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australiaen
dc.identifier.affiliationDepartment of Radiology, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australiaen
dc.identifier.affiliationSchool of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australiaen
dc.identifier.pubmedid33479036-, Hamed
item.fulltextNo Fulltext-
item.openairetypeJournal Article-
item.languageiso639-1en- Florey Institute of Neuroscience and Mental Health-
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