Deep learning for algorithmic detection of pulmonary hypertension using a combined digital stethoscope and single-lead electrocardiogram

使用组合数字听诊器和单导联心电图进行肺动脉高压算法检测的深度学习

• 批准号: 10705310
• 负责人: Gaurav Choudhary
• 金额: $ 110.58万
• 依托单位: EKO DEVICES, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705310
• 关键词: Address; Adult; Algorithms; Award; Cardiac; Cardiac Catheterization Procedures; Caring; Cellular Phone; Chest; Classification; Clinical; Complex; Data; Data Set; Databases; Detection; Diagnosis; Diagnostic; Disease; Early Diagnosis; Echocardiography; Eko; Electrocardiogram; Enrollment; Evaluation; Functional disorder; Goals; Health Services Accessibility; Hospitalization; Human Resources; Investigation; Label; Lead; Location; Marketing; Measures; Medical Device; Modality; Morbidity - disease rate; Outcome Study; Patient-Focused Outcomes; Patients; Performance; Personal Computers; Phase; Phenotype; Prognosis; Public Health; Pulmonary Hypertension; Quality-Adjusted Life Years; Randomized; Running; Screening procedure; Sensitivity and Specificity; Severities; Signal Transduction; Small Business Innovation Research Grant; Specificity; Stethoscopes; Stream; Symptoms; Tablet Computer; Testing; Time; Training; Validation; care costs; clinical decision support; cloud software; comorbidity; cost; deep learning; deep learning algorithm; design; digital; improved; innovation; mortality; novel; patient screening; point of care; pressure; prototype; pulmonary arterial pressure; research clinical testing; screening; support tools; trial comparing

Abstract This SBIR Direct to Phase II project will develop a deep learning-based clinical decision support algorithm for screening and detecting pulmonary hypertension (PH) using phonocardiogram and electrocardiogram data recording using the Eko DUO Digital Stethoscopes. The screening tool will help to decrease the number of patients with pulmonary hypertension that remain undertreated simply because their condition is not diagnosed. The gold standard diagnostic for pulmonary hypertension is right heart catheterization which is costly, invasive, and requires specialized personnel.To address these challenges, Eko developed the DUO, a digital stethoscope in a handheld form factor with built-in single lead electrocardiogram. The DUO is designed to stream digitized phonocardiograms and electrocardiograms to a smartphone, tablet or personal computer. There, the signal can be analyzed with the decision support algorithm we developed as part of this project. The specific aims of this study are : (1) build a database of matched ECG/PCG recording labeled against right heart catheterization and echocardiograms, and (2) develop and clinically test a deep learning algorithm that can detect PH and stratify its severity. By integrating this deep learning algorithm into Eko’s mobile and cloud software platform, we anticipate this algorithm will enable more accurate screening for pulmonary hypertension in adult patients, leading to earlier diagnosis and better patient outcomes.

抽象的 这个SBIR直接进入第二阶段项目将开发基于深度学习的临床决策支持 使用Phonocartiongram和 使用EKO Duo数字听诊器记录心电图数据。筛选工具 将有助于减少肺动脉高压患者的数量 仅仅因为未诊断出病情而被低估了。黄金标准诊断 肺动脉高压是正确的心脏导管插入术，是昂贵，侵入性的，需要 为了应对这些挑战，Eko开发了二人组，数字 带有内置单铅心电图的手持式外形的听诊器。二人是 旨在将数字化的声音图和心电图的数字化流式传输到智能手机，平板电脑 或个人计算机。在那里，可以使用决策支持算法分析信号 作为该项目的一部分开发。这项研究的具体目的是：（1）建立一个数据库 匹配的ECG/PCG记录标记为右心导管插入术和超声心动图， （2）开发并在临床上测试一种深度学习算法，该算法可以检测并分层pH 严重程度。通过将这种深度学习算法集成到EKO的移动和云软件中 平台，我们预计该算法将使肺部更准确地筛选 成年患者的高血压，导致较早的诊断和更好的患者结局。