Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.
Artificial intelligence
Lesion detection
Machine learning
Neuroimaging
Neuroradiology
Journal
Acta neurochirurgica. Supplement
ISSN: 0065-1419
Titre abrégé: Acta Neurochir Suppl
Pays: Austria
ID NLM: 100962752
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
4
12
2021
pubmed:
5
12
2021
medline:
15
12
2021
Statut:
ppublish
Résumé
This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy.
Identifiants
pubmed: 34862541
doi: 10.1007/978-3-030-85292-4_21
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
171-182Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.
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