Evidence map›Paper›PMID 41717044›Full record

ArticleAdvanced materials technologies2026

Non-Invasive Multidimensional Capacitive Sensing for In Vivo Traumatic Brain Injury Monitoring.

Shawn Kim, Yu-Jen Cheng, Tianyi Li, Nathan White, Xu Wang, Kristyn Ringgold, Lauren Neidig, Younghoon Kwon, Jae-Hyun Chung

Abstract read
In one paragraph

Article in Advanced materials technologies, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shawn KimDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Yu-Jen ChengDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Tianyi LiDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Nathan WhiteDepartment of Emergency Medicine, Resuscitation Engineering Science Unit, University of Washington, Seattle, WA 98195 USA.
Xu WangDepartment of Emergency Medicine, Resuscitation Engineering Science Unit, University of Washington, Seattle, WA 98195 USA.
Kristyn RinggoldDepartment of Emergency Medicine, Resuscitation Engineering Science Unit, University of Washington, Seattle, WA 98195 USA.
Lauren NeidigDepartment of Comparative Medicine, University of Washington, Seattle, WA 98195 USA.
Younghoon KwonDivision of Cardiology, University of Washington, Seattle, WA 98195 USA.
Jae-Hyun ChungDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.

Funding

Sleep Apnea-Specific Nocturnal Blood Pressure Surge to Determine Cardiovascular Risks and Therapeutic Benefits in Patients with Obstructive Sleep ApneaR01HL158765 · NHLBI · UNIVERSITY OF WASHINGTON · PI KWON, YOUNGHOON · 2021 to 2025
$3.5M
Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apneaR21HL167126 · NHLBI · UNIVERSITY OF WASHINGTON · PI AKBILGIC, OGUZ, AZARBARZIN, ALI · 2023 to 2024
$202k
NHLBI NIH HHS R01 HL158765NHLBI NIH HHS R21 HL167126
6 · The paper itself

Abstract

Traumatic brain injury (TBI) is a major cause of death and disability, but invasive intracranial pressure (ICP) monitoring is risky, and current non-invasive methods lack the resolution and reliability needed for continuous clinical use. We present a multidimensional, non-invasive single-electrode capacitance (SEC) sensing system for continuous TBI monitoring. Carbon-nanotube paper composite (CPC) electrodes detect regional ICP changes through permittivity variations driven by cerebrovascular pulsations, cerebrospinal fluid (CSF) thickness changes, and brain tissue microvibrations. Surrogate tissue model testing confirms sensitivity to CSF layer thickness, water layer height, vessel wall thickness, and vessel diameter. In vivo pig testing, we analyze the correlation between multisite SEC signals and ICP to identify potential novel sensing metrics. In TBI experiments, these metrics are further evaluated by examining dynamic, cross-hemispheric relationships using four SEC sensors to capture spatially resolved pathophysiology before and after injury. Statistical and machine-learning (ML) approaches are applied to derive novel digital markers and to estimate conventional indices of cerebral autoregulation and intracranial compliance from non-invasive features. Together, this wearable system enables portable, spatially resolved neurocritical monitoring across diverse clinical environments.

Indexed as

Capacitive sensor arrayIntracranial pressure (ICP) monitoringPorcine TBI modelTraumatic brain injury (TBI)

Identifiers

PMID41717044
PMCPMC12915896

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.