Brain Tissue Pulsations: modelling and machine learning methods for the detection of raised intracranial pressure in adult intensive care.

脑组织脉动：用于检测成人重症监护中颅内压升高的建模和机器学习方法。

基本信息

批准号：
2435538
负责人：
金额：
--
依托单位：
University of Leicester
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2435538
关键词：
Brain Tissue Pulsations modelling machine

项目摘要

Aims: This PhD project aims to develop criteria for the detection of raised Intracranial Pressure (ICP), based on non-invasive ultrasound measurements of brain tissue pulsations, for automated detection of raised ICP in patients with acquired brain injury.Research Plan The brain is formed of soft neural tissue that is perfused with blood, surrounded by cerebrospinal fluid (CSF), and encased within the skull. The pulsating dynamics of brain tissue is determined by the neuroanatomy of the region in which the measurements take place, the compliance of brain tissue, and the dynamics of CSF, venous and arterial blood flow (Fig. 1(a)). The first year of the PhD will involve a review of existing models of cerebral flows dynamics, and familiarisation with the modelling software (Matlab). In the second year, we will explore the hydro-mechanical components coupling cerebral flows to brain tissue pulsatility. Over the first two years, in parallel with the physical modelling task, we will also continue the exploration of the brain pulsatility features with a machine learning approach, which was initiated previously.2,3 In the third year, modelling and machine learning results, will be used to construct a classifier to detect raised ICP. The performance of the classifier will be validated against invasive ICP measurements from head trauma patients at Nottingham QMC, under the supervision of Professor Stuart Smith, who will provide clinical input to the study.This PhD opportunity will be widely advertised amongst undergraduate and final year project students at the University of Leicester, and promising students will be personally approached to identify committed high quality candidates.

目的：该博士项目旨在基于对脑组织搏动的无创超声测量，制定颅内压升高（ICP）的检测标准，以自动检测获得性脑损伤患者的颅内压升高。由软神经组织组成，充满血液，被脑脊液 (CSF) 包围，并包裹在颅骨内。脑组织的脉动动力学由测量区域的神经解剖学、脑组织的顺应性以及脑脊液、静脉和动脉血流的动力学决定（图1（a））。博士学位的第一年将涉及回顾现有的脑流动力学模型，并熟悉建模软件（Matlab）。第二年，我们将探索将脑流与脑组织搏动耦合的流体力学成分。在前两年，与物理建模任务并行，我们还将继续利用之前启动的机器学习方法探索大脑搏动特征。2,3在第三年，建模和机器学习结果，将用于构建分类器来检测升高的 ICP。在 Stuart Smith 教授的监督下，分类器的性能将根据诺丁汉 QMC 头部外伤患者的侵入性 ICP 测量进行验证，Stuart Smith 教授将为该研究提供临床输入。该博士机会将在本科生和最后一年的项目中广泛宣传我们将亲自接触莱斯特大学的学生和有前途的学生，以确定忠诚的高素质候选人。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Are portable ankle brachial pressure index measurement devices suitable for hypertension screening?

DOI：
10.1371/journal.pone.0283281
发表时间：
2023
期刊：
PLOS ONE
影响因子：
3.7
作者：
Janus, Justyna;Nicholls, Jennifer K.;Pallett, Edward;Bown, Matthew;Chung, Emma M. L.
通讯作者：
Chung, Emma M. L.

Emerging Detection Techniques for Large Vessel Occlusion Stroke: A Scoping Review.