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: 10079904
• 负责人: John Maidens
• 金额: $ 93.83万
• 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/10079904
• 关键词: Address; Adopted; Adult; Algorithms; Aortic Valve Stenosis; Area; 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

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