Published February 2021
| Version v1
Journal article
Artificial intelligence and deep learning in neuroradiology: exploring the new frontier
Creators
- 1. McMaster University, Department of Radiology, Hamilton, Ontario (Canada)
- 2. McMaster University, Hamilton General Hospital, Department of Radiology, Hamilton, Ontario (Canada)
Description
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. (author)
Availability note (English)
Available from DOI: https://doi.org/10.1177/0846537120954293Additional details
Identifiers
Publishing Information
- Journal Title
- Canadian Association of Radiologists Journal
- Journal Volume
- 72
- Journal Issue
- 1
- Journal Page Range
- p. 35-44
- ISSN
- 0846-5371
INIS
- Country of Publication
- Canada
- Country of Input or Organization
- Canada
- INIS RN
- 53059747
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- ARTIFICIAL INTELLIGENCE; DIAGNOSTIC TECHNIQUES; IONOGRAPHIC IMAGING; NUCLEAR MEDICINE; RADIOLOGY
- Descriptors DEC
- BIOMEDICAL RADIOGRAPHY; DIAGNOSTIC TECHNIQUES; MEDICINE; NUCLEAR MEDICINE; RADIOLOGY
Optional Information
- Notes
- 65 refs., 2 tabs., 4 figs.