ArticleFrontiers in neurology2018
Application of Machine Learning to Automated Analysis of Cerebral Edema in Large Cohorts of Ischemic Stroke Patients.
Article in Frontiers in neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Predictive Value of Machine Learning Models for Cerebral Edema Risk in Stroke Patients: A Meta-Analysis.Brain and behavior · 2025Pooled it
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.Physical and engineering sciences in medicine · 2026Article
- Integrating Brain Imaging Volumetrics and Quantitative Pupillometry for Predicting Neurologic Deterioration after Large Hemispheric Stroke.Neurocritical care · 2026Article
- A Platform for Automatic Extraction of Imaging Biomarkers in CT Scans from Ischemic and Hemorrhagic Stroke Patients.Journal of imaging informatics in medicine · 2026Article
- White Matter Hyperintensities on Admission CT are Associated with Worse Outcomes But Not Cerebral Edema or Hemorrhagic Transformation After Large Vessel Occlusion Stroke.Neurocritical care · 2026Article
- White Matter Hyperintensities on Admission CT Are Associated with Worse Outcomes but not Cerebral Edema or Hemorrhagic Transformation after Large Vessel Occlusion Stroke.Research square · 2026Article
- Targeting TRPV4 to restore glymphatic system function and alleviate cerebral edema in ischemic stroke.Brain pathology (Zurich, Switzerland) · 2025Article
- Utilizing Artificial Intelligence for CSF Segmentation and Analysis in Head CT Imaging: A Systematic Review.Brain sciences · 2025Review
- Integrating Brain Imaging Volumetrics and Quantitative Pupillometry for Predicting Neurologic Deterioration after Large Hemispheric Stroke.Research square · 2025Article
- Artificial intelligence in stroke risk assessment and management via retinal imaging.Frontiers in computational neuroscience · 2025Review
- Evaluating the interaction between hemorrhagic transformation and cerebral edema on functional outcome after ischemic stroke.Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association · 2024Article
- The new era of artificial intelligence in neuroradiology: current research and promising tools.Arquivos de neuro-psiquiatria · 2024Article
- CSF-Based Volumetric Imaging Biomarkers Highlight Incidence and Risk Factors for Cerebral Edema After Ischemic Stroke.Neurocritical care · 2024Article
- A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study.Frontiers in neuroscience · 2024Article
- Novel advanced imaging techniques for cerebral oedema.Frontiers in neurology · 2024Review
- Artificial intelligence applied in acute ischemic stroke: from child to elderly.La Radiologia medica · 2024Review
- Intracranial pressure elevation post-stroke: Mechanisms and consequences.Frontiers in stroke · 2023Review
- Identification of successful cerebral reperfusions (mTICI ≥2b) using an artificial intelligence strategy.Neuroradiology · 2022Article
- MRI Radiomics Features From Infarction and Cerebrospinal Fluid for Prediction of Cerebral Edema After Acute Ischemic Stroke.Frontiers in aging neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Cerebral edema contributes to neurological deterioration and death after hemispheric stroke but there remains no effective means of preventing or accurately predicting its occurrence. Big data approaches may provide insights into the biologic variability and genetic contributions to severity and time course of cerebral edema. These methods require quantitative analyses of edema severity across large cohorts of stroke patients. We have proposed that changes in cerebrospinal fluid (CSF) volume over time may represent a sensitive and dynamic marker of edema progression that can be measured from routinely available CT scans. To facilitate and scale up such approaches we have created a machine learning algorithm capable of segmenting and measuring CSF volume from serial CT scans of stroke patients. We now present results of our preliminary processing pipeline that was able to efficiently extract CSF volumetrics from an initial cohort of 155 subjects enrolled in a prospective longitudinal stroke study. We demonstrate a high degree of reproducibility in total cranial volume registration between scans (
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.