Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for...
Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia
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San Francisco: Public Library of Science
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Language
English
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San Francisco: Public Library of Science
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Purpose To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. Methods This retrospective, observational, single-institution study investigated the diagn...
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Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia
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TN_cdi_plos_journals_2714858423
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2714858423
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ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0274562
