Unsupervised machine learning identifies clinically relevant patterns of CSF dynamic dysfunction in normal pressure hydrocephalus Article Swipe
Emanuele Camerucci
,
Petrice M. Cogswell
,
Jeffrey L. Gunter
,
Matthew L. Senjem
,
Matthew C. Murphy
,
Jonathan Graff-Radford
,
Ignacio Jusué-Torres
,
David T. Jones
,
Jeremy K. Cutsforth‐Gregory
,
Benjamin D. Elder
,
Clifford R. Jack
,
John Huston
,
Hugo Botha
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.clineuro.2025.109162
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.clineuro.2025.109162
NMF-generated patterns of CSF distribution showed high accuracy in discerning individuals with iNPH from controls. The patterns most relying on DESH features showed highest potential for independently predicting NPH diagnosis. The algorithm we proposed should not be perceived as a replacement for human expertise but rather as an additional tool to assist clinicians in achieving accurate diagnoses.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.clineuro.2025.109162
- OA Status
- hybrid
- References
- 24
- OpenAlex ID
- https://openalex.org/W4414267274
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