Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.

Autor: Yeo M; School of Medicine, University of Melbourne, Melbourne, Victoria, Australia., Kok HK; Interventional Radiology Service, Department of Radiology, Northern Health, Melbourne, Victoria, Australia.; School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia., Kutaiba N; Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia., Maingard J; School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia.; Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia.; Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia., Thijs V; Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.; Department of Neurology, Austin Health, Melbourne, Victoria, Australia., Tahayori B; Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.; IBM Research Australia, Melbourne, Victoria, Australia., Russell J; Department of Neurosurgery, Austin Hospital, Melbourne, Victoria, Australia., Jhamb A; Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia., Chandra RV; Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia.; Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia., Brooks M; School of Medicine, University of Melbourne, Melbourne, Victoria, Australia.; School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia.; Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.; Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia., Barras CD; South Australian Institute of Health and Medical Research, Adelaide, South Australia, Australia.; School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia., Asadi H; School of Medicine, Faculty of Health, Deakin University, Burwood, Victoria, Australia.; Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia.; Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia.; Stroke Theme, Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.; Department of Radiology, St Vincent's Hospital, Melbourne, Victoria, Australia.; Interventional Neuroradiology Service, Department of Radiology, Austin Hospital, Melbourne, Victoria, Australia.
Jazyk: angličtina
Zdroj: Journal of medical imaging and radiation oncology [J Med Imaging Radiat Oncol] 2021 May 28. Date of Electronic Publication: 2021 May 28.
DOI: 10.1111/1754-9485.13193
Abstrakt: Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.
(© 2021 The Royal Australian and New Zealand College of Radiologists.)
Databáze: MEDLINE
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