A multi-modal approach for efficient, point-of-care screening of hypertrophic cardiomyopathy

一种高效、即时筛查肥厚型心肌病的多模式方法

基本信息

批准号：
10749588
负责人：
Evangelos Oikonomou
金额：
$ 7.63万
依托单位：
YALE UNIVERSITY
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：
2023-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10749588
关键词：
Address Affect Algorithms Amyloid Anatomy Artificial Intelligence Awareness Board Certification Cardiac Cardiovascular system Case/Control Studies Clinical Clinical Informatics Communities Computer Models Computer Vision Systems Computing Methodologies Consumption Data Data Set Detection Development Devices Diagnosis Diagnostic tests Disease Early Diagnosis Echocardiography Electrocardiogram Electronic Health Record Eligibility Determination Emergency department visit Ensure Event Fellowship General Population Generations Goals Health Health system Hypertrophic Cardiomyopathy Image Individual Label Lead Learning Left Ventricular Hypertrophy Libraries Machine Learning Magnetic Resonance Magnetic Resonance Imaging Maps Medical Informatics Mentors Mentorship Methods Modality Modeling Morbidity - disease rate Morphologic artifacts Noise Outcome Pathway interactions Patients Performance Phenotype Population Population Heterogeneity Postdoctoral Fellow Prevalence Process Provider Randomized, Controlled Trials Research Resource-limited setting Retrospective cohort Risk Screening procedure Signal Transduction Specialized Center Structure Technology Testing Time Training Two-Dimensional Echocardiography Ultrasonography advanced analytics autoencoder biobank cardiac magnetic resonance imaging career clinical heterogeneity computer science computing resources cost deep learning model denoising design diagnostic algorithm empowerment experience feasibility testing genetic testing heart dimension/size hypertensive heart disease implementation science improved improved outcome inherited cardiomyopathy machine learning algorithm medical schools mortality multimodality novel novel therapeutics point of care portability prevent primary care clinic prospective recruit screening screening program simulation skills spectrograph sudden cardiac death supervised learning tool two-dimensional ultrasound underserved community user-friendly vision science wearable device

项目摘要

PROJECT SUMMARY Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy, affecting up to 0.5% of the general population. HCM confers an increased risk of morbidity and mortality but remains clinically underrecognized. Traditionally, the diagnosis of HCM has relied on comprehensive assessment by echocardiography or magnetic resonance imaging, modalities which are not available for screening of the general population. As novel disease-modifying therapies emerge, there is a need for efficient strategies to improve HCM screening outside specialized centers. The research proposed in this post-doctoral fellowship will leverage advanced computational methods and the expanding availability of wearable and portable technologies to adapt machine learning algorithms for the efficient, point-of-care screening of HCM. In Aim 1, the applicant proposes to use a large electrocardiographic (ECG) library to adapt ECG signals for use with wearable devices and fine-tune those signals for the detection of HCM. Noising-denoising algorithms and cross-modal pre-training with corresponding echocardiographic and cardiac magnetic resonance videos will ensure that the models are robust to noise and learn key representations of the HCM phenotype, respectively. In Aim 2, single-view, two- dimensional echocardiographic videos will be extracted, pre-processed, and augmented to simulate point-of- care image acquisition. Through a self-supervised, contrastive pre-training approach, the applicant will build data-efficient computational models to screen for HCM based on echocardiographic videos reflecting the quality and unique challenges seen with point-of-care use. In Aim 3, the applicant proposes a prospective case-control study of patients with and without HCM, who will undergo point-of-care electrocardiography and echocardiography, to test the feasibility and real-world performance of a two-stage HCM screening protocol based on Aims 1 and 2. The proposal is supported by strong mentorship from experts in biomedical machine learning, computer vision, and implementation science. The Yale School of Medicine offers the facilities and computational resources necessary to accomplish the research goals, whereas the Yale-New Haven Health electronic health record and well-phenotyped echocardiographic and ECG libraries ensure access to a diverse and representative population. The proposed period of mentored research will support the applicant’s training in computer vision, advanced analytics, and medical informatics. The experience, data, and skillset acquired during this period will further support the applicant in preparing for a successful career in the implementation science of cardiovascular artificial intelligence technologies.

项目摘要肥厚性心肌病（HCM）是最常见的遗传性心肌病，影响多达0.5％一般人口。 HCM承认发病率和死亡率的风险增加，但在临床上仍保持未被认可。传统上，HCM的诊断已在全面评估中放松超声心动图或磁共振成像，无法筛选的方式一般人口。随着新型疾病改良疗法的出现，需要有效的策略改善HCM筛查专业中心之外。这项博士后奖学金中提出的研究将利用高级计算方法以及可穿戴和便携式技术的扩大可用性适应机器学习算法，以进行HCM的高效，即时筛选。在AIM 1中，适用建议使用大型心电图（ECG）库来适应ECG信号，以与可穿戴设备一起使用并微调这些信号以检测HCM。 Noising-denoising算法和跨模式预训练具有相应的超声心动图和心脏磁共振视频将确保模型是噪声稳健，分别学习HCM表型的关键表示。在AIM 2中，单视，两个尺寸超声心动图视频将被提取，预处理和增强以模拟 - 护理图像获取。通过一种自我监督，对比的预训练方法，申请人将建立基于超声心动图视频反映质量的数据效率计算模型以筛选HCM 以及护理点使用所带来的独特挑战。在AIM 3中，适用的提案预期的案件对照对患有和没有HCM的患者的研究，他们将进行心电图的心电图和超声心动图，以测试两阶段HCM筛选协议的可行性和现实性能基于目标1和2。该提案得到了生物医学专家的强烈精神训练的支持学习，计算机视觉和实施科学。耶鲁大学医学院提供设施和实现研究目标所必需的计算资源，而耶鲁大学新避风港健康电子健康记录和良好的超声心动图和心电图库可确保进入潜水员并代表人口。拟议的修订研究时期将支持申请人的培训计算机视觉，高级分析和医学信息。在期间获得的经验，数据和技能这一时期将进一步支持该申请，以准备在实施科学领域成功职业心血管人工智能技术。