# Integrate Dynamic System Model and Machine Learning for Calibration-Free Noninvasive ICP

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $529,544

## Abstract

Project Summary
 No clinical device exists for noninvasive intracranial pressure (nICP) assessment. Past attempts have
focused on identifying ICP-related signals that are noninvasively measureable, but have done little to address
the calibration problem. Without calibration, only ICP trending can be inferred at the best. However,
noninvasive calibration is not trivial. A universal calibration will fail because individual patients require different
calibration to obtain accurate results. On the other hand, the use of plain regression for individualized
calibration is infeasible because ICP cannot be obtained noninvasively for a de novo patient to begin with.
 Invasive ICP monitoring remains a standard of care and this can be leveraged to continuously grow a
database of ICP, noninvasive signals, and different calibration equations, e.g., each built from a pair of invasive
ICP and noninvasive signal in the database. Then nICP becomes feasible by selecting from a rich set of
calibration equations the optimal choice for a de novo patient. In this project, we will pursue three aims that will
lead to the development of an accurate noninvasive ICP system based on Transcranial Doppler. These aims
are: 1) To implement and validate core algorithms needed for achieving accurate nICP; 2) To test if estimated
nICP is sensitive to variations in ultrasound probe placement; 3) To test the generalizability of the proposed
nICP approach.
 Large epidemiologic surveys reveal that ICP is monitored in only about 58% of US patients when ICP
monitoring is indicated. It is a smaller percentage (37%) in European patients and even fewer in developing
countries. The proposed nICP approach does not have the high risks associated with invasive ICP, requires no
onsite neurosurgical expertise, and can be economically deployed and readily practiced. Therefore, its
potential impact is enormous.

## Key facts

- **NIH application ID:** 10219683
- **Project number:** 7R01NS106905-03
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Xiao Hu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $529,544
- **Award type:** 7
- **Project period:** 2020-08-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10219683

## Citation

> US National Institutes of Health, RePORTER application 10219683, Integrate Dynamic System Model and Machine Learning for Calibration-Free Noninvasive ICP (7R01NS106905-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10219683. Licensed CC0.

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