ArticleAnnals of biomedical engineering2019
Data-Augmented Modeling of Intracranial Pressure.
Article in Annals of biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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.
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Who cites it
15 citing papers in PubMed.
- Advances in Multimodality Monitoring in Traumatic Brain Injury.Current neurology and neuroscience reports · 2026Review
- A mechanistic approach for intracranial pressure (ICP) level estimation.Scientific reports · 2025Article
- Hypertension, blood-brain barrier disruption and changes in intracranial pressure.The Journal of physiology · 2025Review
- Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management.medRxiv : the preprint server for health sciences · 2024Article
- Intracranial pressure-flow relationships in traumatic brain injury patients expose gaps in the tenets of models and pressure-oriented management.Frontiers in physiology · 2024Article
- Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC.Journal of biomechanical engineering · 2022Article
- Determining Clinically-Viable Biomarkers for Ischaemic Stroke Through a Mechanistic and Machine Learning Approach.Annals of biomedical engineering · 2022Article
- Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales.JMIR medical informatics · 2021Article
- Validation of a Non-invasive Inverse Problem-Solving Method for Stroke Volume.Frontiers in physiology · 2021Article
- A Distributed Lumped Parameter Model of Blood Flow.Annals of biomedical engineering · 2020Article
- Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.Physiological measurement · 2020Article
- A Spectral Approach to Model-Based Noninvasive Intracranial Pressure Estimation.IEEE journal of biomedical and health informatics · 2020Article
- Review: pathophysiology of intracranial hypertension and noninvasive intracranial pressure monitoring.Fluids and barriers of the CNS · 2020Review
- A computational bio-chemo-mechanical model of in vivo tissue-engineered vascular graft development.Integrative biology : quantitative biosciences from nano to macro · 2020Article
- Identification of Pulse Onset on Cerebral Blood Flow Velocity Waveforms: A Comparative Study.BioMed research international · 2019Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction.
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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.