A Machine Learning Approach to Classifying Time Since Stroke using Medical Imaging

使用医学成像对中风后时间进行分类的机器学习方法

• 批准号: 10363751
• 负责人: Corey Wells Arnold
• 金额: $ 42.99万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10363751
• 关键词: Adverse event; Affect; Agreement; Algorithms; Alteplase; American; Area; Attenuated; Blood flow; Brain; California; Cause of Death; Characteristics; Classification; Clinical; Collaborations; Complex; Computer Analysis; Computing Methodologies; Data; Decision Making; Diffusion Magnetic Resonance Imaging; Eligibility Determination; Engineering; Environment; Foundations; Functional disorder; Goals; Guidelines; Hemorrhage; Hour; Image; Image Analysis; Imaging Device; Imaging Techniques; Individual; Infarction; Intravenous; Learning; Liquid substance; Location; Machine Learning; Magnetic Resonance; Maps; Medical Imaging; Methods; Modeling; Morbidity - disease rate; Multimodal Imaging; Neurologist; Patient imaging; Patients; Perfusion; Phenotype; Physicians; Physiological; Play; Process; Publishing; Recovery; Recurrence; Reperfusion Therapy; Research; Risk; Role; Savings; Secondary to; Shapes; Signal Transduction; Spectrum Analysis; Stroke; Techniques; Thrombolytic Therapy; Time; Tissues; Training; United States; Universities; Visualization; Visualization software; Work; X-Ray Computed Tomography; acute stroke; artery occlusion; autoencoder; base; clinical imaging; cohort; deep learning; diffusion weighted; experience; improved; interest; learning strategy; machine learning framework; machine learning method; machine learning model; mortality; multimodality; novel; outcome prediction; patient population; perfusion imaging; prevent; prospective; radiologist; spatiotemporal; spectrograph; stroke patient; stroke symptom; tool

PROJECT SUMMARY/ABSTRACT Stroke is a leading cause of mortality and morbidity in the United States, with approximately 795,000 Americans experiencing a new or recurrent stroke each year. Intravenous tissue plasminogen activator (IV tPA) is the dominant and most proven treatment option, but its use is only indicated within 4.5 hours following a stroke. Unfortunately, up to 30% of stroke patients present with an unknown time since stroke (TSS) symptom onset, which makes them ineligible to receive IV tPA. Many of these individuals could be spared severe morbidity or mortality if there existed an alternative method for establishing TSS, allowing them to be identified and treated. This proposal will develop machine learning methods to create a physiologically grounded method for predicting TSS based on multiparametric magnetic resonance (MR) and computed tomography (CT) imaging data. We believe our proposed techniques will outperform state-of-the-art methods that are based on subjective image interpretation, and have the potential to provide an objective data point that may be used in conjunction with the subjective assessments of experts, or in clinical environments that lack expertise in stroke imaging Research has established that MR and CT imaging captures information that correlates with TSS. However, existing methods for extracting this information are based on a physician subjectively interpreting the images and delineating regions of interest, processes that have been documented to have only weak to moderate agreement across trained expert reviewers. An automated approach that comprehensively analyzes the spectrum of imaging data could identify complex relationships across channels that more accurately classify TSS. For example, in MR, diffusion-weighted, perfusion-weighted, and fluid attenuated inversion recovery imaging all play important roles in characterizing a stroke, but a deep understanding of how each channel may be combined to describe TSS is unknown. We propose to establish new deep learning methods for fusing this information. Specifically, we will: 1) develop a machine learning framework for classifying TSS; 2) develop a deep convolutional autoencoder to generate novel multimodal image representations from MR and CT to improve classification; and 3) implement visualization techniques that elucidate the relationship between deep features and pathophysiological stroke processes. Under this project, we will use data from the UCLA and UCI Stroke Centers, allowing us to study different patient populations and imaging techniques. The successful completion of this research will provide a new method for estimating TSS from imaging, leading to new prospective trials for providing therapy to patients with unknown TSS.

项目摘要/摘要 中风是美国死亡率和发病率的主要原因，约有79.5万 美国人每年经历新的或经常性的中风。静脉组织纤溶酶原活化剂（IV TPA）是主要且最有经验的治疗方法，但仅在4.5小时内显示其使用 中风。不幸的是，自中风（TSS）症状以来，多达30％的中风患者出现未知时间 发作，这使得他们没有资格接收IV TPA。其中许多人可能会幸免 发病率或死亡率，如果存在一种替代方法来建立TSS，则可以识别它们 并得到治疗。该建议将开发机器学习方法来创建一种生理基础的方法 用于预测基于多参数磁共振（MR）和计算机断层扫描（CT）的TSS 成像数据。我们认为，我们提出的技术将胜过基于的最先进方法 主观图像解释，并有可能提供可能使用的客观数据点 与专家的主观评估或中风缺乏专业知识的主观评估 成像 研究表明，MR和CT成像捕获了与TSS相关的信息。然而， 提取此信息的现有方法基于主观解释图像的医师 并描绘了感兴趣的区域，已记录的过程只有弱至中等 跨训练的专家审阅者达成协议。一种自动化的方法，可全面分析 成像数据的频谱可以识别跨通道的复杂关系，以更准确地进行分类 TSS。例如，在MR中，扩散加权，灌注加权和流体减弱了反转恢复 成像所有在表征中风的重要作用 组合以描述TSS是未知的。我们建议建立新的深度学习方法来融合这一点 信息。具体来说，我们将：1）开发用于对TSS进行分类的机器学习框架； 2）发展 深卷积自动编码器，从MR和CT生成新颖的多模式图像表示 改善分类； 3）实施可视化技术，阐明了深层之间的关系 特征和病理生理中风过程。在此项目下，我们将使用UCLA和UCI的数据 中风中心，使我们能够研究不同的患者人群和成像技术。成功 这项研究的完成将为估算来自成像的TSS的新方法，从而导致新的 为未知TSS患者提供治疗的前瞻性试验。

项目成果

A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.

• DOI: 10.1109/bhi50953.2021.9508597
• 发表时间: 2021-07
• 期刊: ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics
• 作者: Zhang, Haoyue;Polson, Jennifer;Nael, Kambiz;Salamon, Noriko;Yoo, Bryan;Speier, William;Arnold, Corey
• 通讯作者: Arnold, Corey