ArticleNPJ digital medicine2025
Machine learning approach for noninvasive intracranial pressure estimation using pulsatile cranial expansion waveforms.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Spontaneous Middle Cranial Fossa Cerebrospinal Fluid Leaks and Intracranial Hypertension: Systematic Review With Meta-Analysis.Laryngoscope investigative otolaryngology · 2026Review
- Neuroworsening in traumatic brain injury: A consensus of the Latin American Brain Injury Consortium (LABIC) and the Latin American Federation of Neurosurgical Societies (FLANC) expert group.Neurosurgical review · 2026Article
- Non-invasive intracranial pressure estimation in the intensive care unit: narrative review of methods and clinical applications.Intensive care medicine · 2026Review
- Non-Invasive Multidimensional Capacitive Sensing for In Vivo Traumatic Brain Injury Monitoring.Advanced materials technologies · 2026Article
- Application of a time series foundation model to noninvasively estimate intracranial pressure.Journal of clinical monitoring and computing · 2025Article
- Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions.Bioengineering (Basel, Switzerland) · 2025Review
- Monro-Kellie 4.0: moving from intracranial pressure to intracranial dynamics.Critical care (London, England) · 2025Review
- Traumatic brain injury management in the intensive care unit: standard of care and knowledge gaps.Intensive care medicine · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Noninvasive methods for intracranial pressure (ICP) monitoring have emerged, but none has successfully replaced invasive techniques. This observational study developed and tested a machine learning (ML) model to estimate ICP using waveforms from a cranial extensometer device (brain4care [B4C] System). The model explored multiple waveform parameters to optimize mean ICP estimation. Data from 112 neurocritical patients with acute brain injuries were used, with 92 patients randomly assigned to training and testing, and 20 reserved for independent validation. The ML model achieved a mean absolute error of 3.00 mmHg, with a 95% confidence interval within ±7.5 mmHg. Approximately 72% of estimates from the validation sample were within 0-4 mmHg of invasive ICP values. This proof-of-concept study demonstrates that noninvasive ICP estimation via the B4C System and ML is feasible. Prospective studies are needed to validate the model's clinical utility across diverse settings.
Identifiers
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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.