Machine learning identifies stroke features between species.
Algorithms
Animals
Brain
/ pathology
Brain Ischemia
/ pathology
Diffusion Magnetic Resonance Imaging
/ methods
Disease Models, Animal
Humans
Image Processing, Computer-Assisted
/ methods
Infarction, Middle Cerebral Artery
/ diagnosis
Machine Learning
Male
Rats
Rats, Sprague-Dawley
Stroke
/ diagnosis
Ischemic stroke
Machine learning
Stroke segmentation Neuroimaging
Translational medicine
Journal
Theranostics
ISSN: 1838-7640
Titre abrégé: Theranostics
Pays: Australia
ID NLM: 101552395
Informations de publication
Date de publication:
2021
2021
Historique:
received:
12
08
2020
accepted:
14
12
2020
entrez:
18
1
2021
pubmed:
19
1
2021
medline:
28
7
2021
Statut:
epublish
Résumé
Identification and localization of ischemic stroke (IS) lesions is routinely performed to confirm diagnosis, assess stroke severity, predict disability and plan rehabilitation strategies using magnetic resonance imaging (MRI). In basic research, stroke lesion segmentation is necessary to study complex peri-infarction tissue changes. Moreover, final stroke volume is a critical outcome evaluated in clinical and preclinical experiments to determine therapy or intervention success. Manual segmentations are performed but they require a specialized skill set, are prone to inter-observer variation, are not entirely objective and are often not supported by histology. The task is even more challenging when dealing with large multi-center datasets, multiple experimenters or large animal cohorts. On the other hand, current automatized segmentation approaches often lack histological validation, are not entirely user independent, are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation.
Identifiants
pubmed: 33456586
doi: 10.7150/thno.51887
pii: thnov11p3017
pmc: PMC7806470
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3017-3034Informations de copyright
© The author(s).
Déclaration de conflit d'intérêts
Competing Interests: The authors have declared that no competing interest exists.
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