一种用于自动检测和评估创伤性脑损伤的智能便携式门诊医疗工具包

P. Ganapathy, Shantanu H. Joshi, J. Yadegar, Niranjan Kamat, C. Caluser
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引用次数: 4

摘要

我们建议开发一种便携式、手持式、无创的解决方案,用于在门诊/应急响应场景中准确筛查和实时监测创伤性脑损伤(BI)。提出了一种分层传感概念,统一了A)超声(US)、b)近红外光谱(NIRS)、c)血压计(IOP)等替代模式,以预测BI、BI的严重程度和紧急医疗服务(EMS)人员的建议模式。具体来说,我们的目标是确定i)新的视神经鞘三维形态测量参数,该参数可以从US数据中预测颅内压升高,ii)使用近红外光谱(NIRS)预测颅内血肿的发生率,iii)使用血压计测量眼压,iv)使用其他辅助非侵入性传感模式测量脑血流量和血氧含量,v)最后提供所有i)-iv)组合的传感器融合结果。该决策支持系统(DSS)将通过结合准确的现场测量来改善BI检测,该测量可以解释个体基线变化并监测损伤的时间表现。DSS收集的数据和进行的初步分析将通过无线3G网络发送给驻扎在附近创伤中心的急诊科(ED)医生。根据可用带宽,所有数据(包括初步分析(美国视频,图像,一维测量等)或仅精炼信号(筛选过程中提取的特征向量)将与DSS诊断一起发送给医生。如果DSS确定的输出是医生同意的,那么筛查可以终止,医生/急诊科工作人员可以准备进行高级干预(插管,脑脊液(CSF)引流等)。如果没有,值班医生可以通知医生重复扫描/采取额外的测量,以通过DSS获得更具体的结果。总之,这样一个知识驱动的系统将为新手或训练有素的医生提供一个易于使用的工具来检测创伤性BI,并在进行高级干预之前减少ED的诊断时间(即计算机断层扫描(CT)扫描,临床评估),从而改善预后。
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An intelligent and portable ambulatory medical toolkit for automatic detection and assessment of traumatic brain injuries
We propose to develop a portable, handheld, noninvasive solution for accurate screening and real-time monitoring of traumatic brain injury (BI) in ambulatory/emergency response scenarios. A layered sensing concept that unifies alternate modalities such as a) ultrasound (US), b) near infrared spectroscopy (NIRS), c) tonometry (IOP), to predict BI, their severity and mode of recommendations for emergency medical service (EMS) personnel is offered. Specifically, we aim to determine i) novel 3D morphometric parameters of optic nerve sheath that can predict elevated intracranial pressure from US data, ii) incidence of intracranial hematomas using NIRS, iii) intraocular pressure using a tonometer, iv) cerebral blood flow and blood oxygen content using other auxiliary non-invasive sensing modes and v) finally provide a sensor fused outcome of all i)-iv) combined. This decision-support system (DSS) will improve BI detection by incorporating accurate on-site measurements that accounts for individual baseline variations and monitors temporal manifestation of the injury. The data collected and the preliminary analysis performed by the DSS will be sent to an emergency department (ED) physician stationed at a nearby trauma center via a wireless 3G network. Based on the available bandwidth, either all the data including the preliminary analysis (US video, images, 1D measurements, etc) or only the refined signals (feature vector extracted during screening) along with the DSS diagnosis will be sent to the physician. If the DSS determined output is agreeable to the physician then the screening can be terminated and the physician/ED staff can prepare to perform advanced interventions (intubation, cerebralspinal fluid (CSF) drainage, etc). If not, the on-call physician can inform the medic to repeat the scans/take additional measurements to obtain a more concrete outcome via the DSS. In summary, such a knowledge-driven system will equip a novice or a trained medic with an easy-to-use tool to detect traumatic BI and reduce the diagnosis time involved (i.e., computed tomography (CT) scan, clinical evaluation) in ED before performing advanced interventions and thereby improve the prognosis.
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