Big Data and Deep Learning for the Interictal-Ictal-Injury Contiuum

发作间期-发作期-损伤连续体的大数据和深度学习

基本信息

批准号：
10761842
负责人：
Michael Brandon Westover
金额：
$ 9.1万
依托单位：
BETH ISRAEL DEACONESS MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-07 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10761842
关键词：
Big Data Deep Learning Interictal

项目摘要

Project Summary/Abstract: Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum Brain monitoring in critical care has grown dramatically over the past 20 years with the discovery that a large proportion of ICU patients suffer from subclinical seizures and seizure-like electrical events, collectively called “ictal-interictal-injury continuum abnormalities” (IIICAs), detectable only by electroencephalography (EEG). This growth has created a crisis in critical care: It is clear that IIICAs damage the brain and cause permanent neurologic disability. Yet detection of IIICAs by expert visual review is often delayed suggesting we need better tools for real-time monitoring, to cope with the deluge of ICU EEG data. In other cases, IIICAs appear to be harmless epiphenomena, and many worry that increased awareness of IIICAs has created an epidemic of overly-aggressive prescribing of anticonvulsant drugs leading to preventable adverse events and costs. This crisis highlights critical unmet needs for automated EEG monitoring for IIICAs, and a better understanding of which types of IIICAs cause neural injury and warrant intervention. Causes of IIICAs range widely, from primary brain injuries like hemorrhagic stroke and intracranial hemorrhage, to systemic medical illnesses like sepsis and uremia. Until recently, this massive clinical heterogeneity has been an insurmountable barrier to understanding the impact of IIICAs on neurologic outcome. However, recent advances in deep learning, coupled with the unprecedented availability of a massive dataset developed by our team over the last three years, makes it feasible for the first time to systematically study the relationship between IIICAs and neurologic outcomes. To meet the need for better monitoring tools and better models for understanding IIICAs, we will take a deep learning approach to leverage the as-yet untapped information in a massive ICU EEG dataset. We will pursue three Specific Aims: SA1: Comprehensively label all occurrences of IIICAs in a massive set of cEEG recordings, thus preparing the EEG data for training computers to detect IIICA patterns; SA2: Develop supervised DL algorithms to detect IIICAs as accurately as human experts, thus providing powerful tools for both research on IIICAs and for clinical brain monitoring; SA3: Estimate the effect of IIICAs on neurologic outcome: we will develop models to quantify effects of IIICAs on risk for disability after controlling for inciting illness and other clinical factors, and to predict effects of interventions to suppress IIICAs. This work will provide four crucial benefits to advance the field of precision critical care neurology, and by extension, our ability to provide optimal neurologic care for patients during critical illness. 1) Improved understanding of the clinical significance of seizure like IIICA states; 2) development of robust tools and algorithms for critical care brain telemetry; 3) a unique, massive, publicly available, thoroughly annotated dataset that will enable other researchers to further advance the field; and 4) a testable model that predicts which types of cEEG abnormalities warrant aggressive treatment, setting the stage for interventional trials.

项目摘要/摘要：发作间期-发作期-损伤连续体的大数据和深度学习过去 20 年来，重症监护中的脑部监测得到了显着发展，人们发现，大量部分 ICU 患者患有亚临床癫痫发作和癫痫样电事件，统称为 “发作期-发作间期-损伤连续异常”（IIICA），只能通过脑电图（EEG）检测到。增长造成了重症监护危机：很明显，IIICA 会损害大脑并造成永久性的后果然而，通过专家目视检查对 IIICA 的检测往往会被延迟，这表明我们需要更好的实时监测工具，以应对 ICU 脑电图数据的洪流在其他情况下，IIICA 似乎可以。是无害的副现象，许多人担心 IIICA 意识的增强导致了一种流行病过度积极地开抗惊厥药物会导致可预防的不良事件和费用。危机凸显了 IIICA 自动化脑电图监测的关键未满足需求，以及更好的理解其中哪些类型的 IIICA 会导致神经损伤并需要干预。 IIICA 的病因多种多样，包括出血性中风等原发性脑损伤和颅内损伤出血，败血症和尿毒症等全身性疾病，直到最近，这种大规模的临床。异质性一直是理解 IIICA 对神经系统影响的不可逾越的障碍然而，深度学习的最新进展以及前所未有的可用性。我们的团队在过去三年中开发的海量数据集首次使系统地研究 IIICA 与神经系统结果之间的关系。为了满足对更好的监测工具和更好的模型来理解 IIICA 的需求，我们将采取我们将采用深度学习方法来利用海量 ICU 脑电图数据集中尚未开发的信息。追求三个具体目标： SA1：在大量数据中全面标记所有发生的 IIICA cEEG 记录，从而为训练计算机准备 EEG 数据以检测 IIICA 模式：开发有监督的深度学习算法可以像人类专家一样准确地检测 IIICA，从而提供强大的工具对于 IIICA 的研究和临床大脑监测；SA3：估计 IIICA 的影响神经系统结果：我们将开发模型来量化 IIICA 在控制后对残疾风险的影响诱发疾病和其他临床因素，并预测抑制 IIICA 干预措施的效果。这项工作将为推进精准重症监护神经病学领域提供四个关键的好处，扩展，我们为危重病患者提供最佳神经科护理的能力 1) 提高。了解 IIICA 状态等癫痫发作的临床意义；2) 开发强大的工具和重症监护脑部遥测算法；3) 独特的、大规模的、公开的、完整注释的算法；数据集将使其他研究人员能够进一步推进该领域；4）可测试的预测模型；哪些类型的 cEEG 异常需要积极治疗，为介入试验奠定了基础。