| Literature DB >> 32946272 |
Hussam Kaka1, Euan Zhang2, Nazir Khan2.
Abstract
There 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.Entities:
Keywords: artificial; deep; intelligence; learning; neuroradiology
Year: 2020 PMID: 32946272 DOI: 10.1177/0846537120954293
Source DB: PubMed Journal: Can Assoc Radiol J ISSN: 0846-5371 Impact factor: 2.248