Deep Learning Enabled Endovascular Stroke Therapy Screening in Community Hospitals

深度学习支持社区医院的血管内卒中治疗筛查

基本信息

批准号：
10184809
负责人：
LUCA GIANCARDO
金额：
$ 48.14万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10184809
关键词：
Acute Address Age Algorithms Angiography Architecture Blinded Brain Injuries Bypass California Caring Cause of Death Cessation of life Clinical Clinical Data Clinical Trials Communities Community Hospitals Computer software Country Data Data Set Databases Dependence Detection Development Eligibility Determination Evaluation Foundations Future Goals Guidelines Heterogeneity Hospital Referrals Hospitals Hour Human Image Industry Standard Infarction Infrastructure Institution Intervention Intravenous Ionizing radiation Ischemic Stroke Location Machine Learning Magnetic Resonance Imaging Medical Methods Modality Modeling Neurological outcome Outcome Patient imaging Patient-Focused Outcomes Patients Performance Perfusion Population Heterogeneity Procedures Protocols documentation Race Radiation exposure Reader Reproducibility Research Risk Route Services Software Tools Source Stroke System Testing Texas Therapy Evaluation Time Tissues Training United States Validation base biomedical referral center brain tissue care delivery community center cost deep learning deep neural network disability heterogenous data imaging capabilities imaging modality improved loss of function multimodality multiple datasets neural network architecture neuroimaging novel novel strategies patient screening perfusion imaging post stroke predictive modeling prototype screening sex stroke patient stroke therapy success support tools thrombolysis tool

项目摘要

Project Summary/Abstract Stroke is the 5th leading cause of death in the United States. Endovascular stroke therapy (EST) has revolutionized the management of large vessel occlusion (LVO) acute ischemic stroke (AIS), which accounts for a disproportionate amount of disability in stroke. While this therapy has been shown to significantly improve clinical outcomes in multiple clinical trials, these studies nearly all required screening patients with advanced NeuroImaging such as CT Perfusion (CTP), a modality not available to the majority of community hospitals. As such, there is a pressing need to for a tool able to identify EST candidates leveraging the infrastructure already existing in community hospitals. We envision a software-based service to automate the NeuroImaging evaluation for EST using CT angiography (CTA). We developed and tested a prototype of a novel deep neural network architecture called DeepSymNet. Our preliminary data indicate that uniquely using CTAs we can determine (1) the presence or absence of a large vessel occlusion (2) if the extent of ischemic core and (3) volume of tissue “at risk” (penumbra) is above or below the thresholds used in the clinical trials, when compared to concurrently obtained results using CTP. We will pursue our project goal with three aims: - Aim 1 - Establish one of the largest multi-institution dataset for neuro-imaging research in acute ischemic stroke. We will acquire a multi-center dataset including imaging and clinical data from 15 hospitals across Texas and California, from a range of scanners, imaging acquisition protocols, and hospital types (i.e. large academic and smaller community). - Aim 2 - Develop interpretable deep learning models to determine the eligibility for EST. We will methodically test a set of model architectures, data augmentation strategies, loss functions and pre-processing steps based on DeepSymNet. We will train and test the algorithm against various definitions of infarct core and penumbral volume based on CTP results. This approach will allow for models adaptable to the everchanging definition of EST eligibility. – Aim 3 - Evaluate the external validity of DeepSymNet-based models on a large multi-center independent dataset. To accomplish this aim, we will deploy our DeepSymNet software on patient imaging and data from multiple hospitals, which were not used in the creation of the software. We will also validate our approach of using CTA alone to determine ischemic core by validating blinded reads of infarct core from CTA source images performed by expert readers against concurrently acquired CTP results. Completion of these aims will have a sustained, transformative impact by supporting the creation and validation of decision support tools readily translatable to the patient bedside in the vast majority of community hospitals across the country. In doing so, we hope to expand the access to high-quality EST screening to thousands of additional AIS patients.

项目概要/摘要中风是美国第五大死亡原因。彻底改变了大血管闭塞 (LVO) 急性缺血性中风 (AIS) 的治疗，这说明虽然这种疗法已被证明可以显着改善中风造成的残疾。在多项临床试验中的临床结果中，这些研究几乎都需要筛查晚期患者神经影像，例如 CT 灌注 (CTP)，这种模式大多数社区医院都无法使用。因此，迫切需要一种能够利用现有基础设施识别 EST 候选者的工具我们设想一种基于软件的服务来实现神经影像的自动化。我们开发并测试了一种新型深度神经网络的原型。我们的初步数据表明，我们可以通过独特的 CTA 来实现称为 DeepSymNet 的网络架构。确定 (1) 是否存在大血管闭塞 (2) 核心缺血程度以及 (3) 当“处于风险”（半影）的组织体积高于或低于临床试验中使用的阈值时与使用 CTP 同时获得的结果进行比较。我们将通过三个目标来实现我们的项目目标： - 目标 1 - 建立最大的多机构数据集之一，用于急性缺血性神经影像研究我们将获取包括来自 15 家医院的影像和临床数据的多中心数据集。德克萨斯州和加利福尼亚州，来自一系列扫描仪、成像采集协议和医院类型（即大型医院）学术界和较小的社区）。 - 目标 2 - 开发可解释的深度学习模型来确定 EST 的资格。测试一组模型架构、数据增强策略、损失函数和预处理步骤在 DeepSymNet 上，我们将根据梗塞核心和半影的各种定义来训练和测试算法。这种方法将允许模型适应不断变化的定义。 EST 资格。 – 目标 3 - 评估基于 DeepSymNet 的模型在大型多中心独立模型上的外部有效性为了实现这一目标，我们将在患者成像和数据上部署 DeepSymNet 软件。我们还将验证我们的方法，这些医院在创建软件时未使用。单独使用 CTA 通过验证来自 CTA 源的梗塞核心的盲读来确定缺血核心由专家读者根据同时获取的 CTP 结果执行的图像。完成这些目标将通过支持创造和发展产生持续的、变革性的影响。验证决策支持工具可轻松应用于绝大多数社区的患者床边在此过程中，我们希望扩大高质量 EST 筛查的覆盖范围。数千名额外的 AIS 患者。