Deep Learning for Automated Aortic Stenosis and Valvular Heart Disease Detection Using a Digital Stethoscope

使用数字听诊器进行深度学习自动主动脉瓣狭窄和瓣膜性心脏病检测

• 批准号: 10425344
• 负责人: James David Thomas
• 金额: $ 92.08万
• 依托单位: EKO DEVICES, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10425344
• 关键词: Address; Adopted; Adult; Algorithms; Aortic Valve Stenosis; Auscultation; Cardiac; Caring; Cellular Phone; Clinic; Clinical; Clinical Data; Collection; Computer software; Data; Data Collection; Databases; Detection; Device Designs; Diagnosis; Disease; Early Diagnosis; Echocardiography; Eko; Electrocardiogram; Enrollment; Environment; Goals; Gold; Health Personnel; Heart Sounds; Heart Valve Diseases; Heart murmur; Institution; Label; Lead; Medical Device; Methods; Mitral Valve Insufficiency; Modeling; Network-based; Outcome Study; Pathology; Patient-Focused Outcomes; Patients; Performance; Personal Computers; Phase; Prevalence; Primary Health Care; Public Health; Reporting; Resources; Screening procedure; Sensitivity and Specificity; Severities; Signal Transduction; Site; Small Business Innovation Research Grant; Specificity; Stethoscopes; Stream; Supervision; System; Tablet Computer; Testing; Training; Tricuspid Valve Insufficiency; accurate diagnosis; algorithm training; automated algorithm; base; cardiovascular health; clinical decision support; clinical development; clinical research site; clinically significant; cloud software; convolutional neural network; cost; deep learning; deep learning algorithm; deep neural network; diagnosis standard; digital; experience; innovation; learning strategy; medical specialties; point of care; public health relevance; screening; sound; underserved area

Abstract This SBIR Phase II project will develop a deep learning-based clinical decision support algorithm for detecting and diagnosing valvular heart disease based on heart sounds recorded using the Eko Core and Eko Duo Digital Stethoscopes. This screening tool will help to decrease the number of patients with valvular heart disease that remain undertreated simply because their condition is not diagnosed. Auscultation is commonly the method by which valvular heart disease is first detected, but cases often fail to be referred to echocardiography for diagnosis because clinicians fail to detect heart murmurs, particularly in noisy or rushed environments. To address this challenge, Eko had developed the Core, a digital stethoscope attachment that can be added in-line to a clinician’s existing stethoscope that amplifies heart sounds and Duo, a digital stethoscope in a handheld form factor with built-in single lead electrocardiogram. Both devices are designed to stream digitized phonocardiograms to a smartphone, tablet or personal computer. There, the signal can be analyzed with the decision support algorithm we will develop as part of this project. The specific aims of this study are: (1) to collect a database with condition- specific recording labels to enable deep learning for heart sounds though clinical data collection at six clinical sites, and (2) to develop and evaluate a collection of deep convolutional neural network-based algorithms trained on the database. These algorithms will (2a) distinguish between systolic, diastolic and continuous murmurs, (2b) classify aortic stenosis (AS), mitral regurgitation (MR), tricuspid regurgitation (TR), and innocent murmurs (2c) assess the severity of AS, MR and TR. By integrating these deep learning algorithms into Eko's mobile and cloud software platform, currently used by clinicians at over 1000 institutions worldwide, we anticipate this algorithm will enable more accurate screening for valvular heart disease in adult patients, leading to earlier diagnosis and better patient outcomes.

摘要 SBIR第二阶段项目将开发一种基于深度学习的临床决策支持算法，用于检测 以及基于使用Eko Core和Eko Duo Digital记录的心音诊断心脏瓣膜病 听诊器。这种筛查工具将有助于减少心脏瓣膜病患者的数量， 仅仅因为他们的病情没有得到诊断，他们的治疗仍然不足。听诊通常是通过 心脏瓣膜病首先被发现，但病例往往无法通过超声心动图进行诊断 因为临床医生不能检测心脏杂音，特别是在嘈杂或匆忙的环境中。为了解决这个 为了应对挑战，Eko开发了Core，这是一种数字听诊器附件，可以在线添加到临床医生的 现有的放大心音的听诊器和Duo，一种手持形状因子的数字听诊器， 内置单导联心电图。这两种设备都被设计成将数字化心音图流传输到 智能手机、平板电脑或个人电脑。在那里，可以用决策支持算法分析信号 我们将把它作为这个项目的一部分。本研究的具体目的是：（1）收集一个条件- 特定的录音标签，通过在六个临床中心收集临床数据，实现对心音的深度学习 网站，以及（2）开发和评估一系列基于深度卷积神经网络的算法 在数据库上训练。这些算法将（2a）区分收缩期、舒张期和连续期 杂音，（2b）分类主动脉瓣狭窄（AS），二尖瓣返流（MR），三尖瓣返流（TR），和无辜 杂音（2c）评估AS、MR和TR的严重程度。通过将这些深度学习算法集成到Eko的 移动的和云软件平台，目前由全球1000多家机构的临床医生使用，我们 预计该算法将能够更准确地筛查成年患者的心脏瓣膜病， 更早的诊断和更好的患者结果。

项目成果

Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.

• DOI: 10.1161/jaha.123.030377
• 发表时间: 2023-10-17
• 期刊: Journal of the American Heart Association
• 影响因子: 5.4

Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform.

通过数字听诊器平台自动检测心脏杂音的深度学习算法。

• DOI: 10.1161/jaha.120.019905
• 发表时间: 2021-05-04
• 期刊: Journal of the American Heart Association
• 影响因子: 5.4
• 作者: Chorba JS;Shapiro AM;Le L;Maidens J;Prince J;Pham S;Kanzawa MM;Barbosa DN;Currie C;Brooks C;White BE;Huskin A;Paek J;Geocaris J;Elnathan D;Ronquillo R;Kim R;Alam ZH;Mahadevan VS;Fuller SG;Stalker GW;Bravo SA;Jean D;Lee JJ;Gjergjindreaj M;Mihos CG;Forman ST;Venkatraman S;McCarthy PM;Thomas JD
• 通讯作者: Thomas JD