ArticleClinical neurology and neurosurgery2025
Unsupervised machine learning identifies clinically relevant patterns of CSF dynamic dysfunction in normal pressure hydrocephalus.
Article in Clinical neurology and neurosurgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Moving on in hydrocephalus imaging: from 2D to 3D biomarkers.Acta neurochirurgica · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
backgroundIdiopathic normal pressure hydrocephalus (iNPH) is a common and debilitating condition whose diagnosis is made challenging due to the unspecific and common clinical presentation. The aim of our study was to determine if data driven patterns of cerebrospinal fluid (CSF) distribution can be used to predict iNPH diagnosis and response to treatment.
methodsWe established a cohort of iNPH patients and age/sex-matched controls. We used Non-negative Matrix Factorization (NMF) on CSF probability maps from segmentation of T1-weighted MRI to obtain patterns or components of CSF distribution across participants and a load on each component in each participant. Visual assessment of morphologic phenotype was performed by a neuroradiologist, and clinical symptom improvement was assessed via retrospective chart review. We used the NMF component loads to predict diagnosis and clinical outcome after ventriculoperitoneal shunt placement for treatment of iNPH. Similar models were developed using manual Evan's index and callosal angle measurements.
resultsWe included 98 iNPH patients and 98 controls split into test (20 %) and train (80 %) sets. The optimal NMF decomposition identified 7 patterns of CSF distribution in our cohort. Accuracy for predicting a clinical diagnosis of iNPH using the automated NMF model was 96 %/97 % in the train/test sets, which was similar to the performance of the manual measure models (92 %/97 %). Visualizing the voxels that contributed most to the NMF models revealed that the voxels most associated with a disproportionately enlarged subarachnoid space hydrocephalus (DESH) were the ones with higher probability of iNPH diagnosis. Neither NMF nor manual metrics performed well for prediction of qualitative clinical outcomes.
conclusionsNMF-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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.