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- Hussam Kaka, Euan Zhang, and Nazir Khan.
- Department of Radiology, 3710McMaster University, Hamilton, Ontario, Canada.
- Can Assoc Radiol J. 2021 Feb 1; 72 (1): 35-44.
AbstractThere have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.
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