Smart stethoscope for monitoring and diagnosis of lung diseases

智能听诊器监测和诊断肺部疾病

• 批准号: 9158273
• 负责人: Mounya Elhilali
• 金额: $ 58.74万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9158273
• 关键词: Accident and Emergency department; Acoustics; Acute; Affect; Age-Years; Algorithms; Antibiotics; Area; Auscultation; Benchmarking; Care Technology Points; Case Management; Cessation of life; Chest; Child; Childhood; Classification; Clinic; Clinical; Clinics and Hospitals; Communities; Complement; Computer Assisted; Computer-Assisted Diagnosis; Cues; Detection; Development; Devices; Diagnosis; Diagnostic; Effectiveness; Emergency Situation; Engineering; Environment; Exposure to; Goals; HIV; Health; Health Personnel; Home environment; Hospitals; Infection; Life; Lower Respiratory Tract Infection; Lung; Lung diseases; Malaria; Marketing; Masks; Medical; Methods; Modification; Monitor; Noise; Outcome; Palpation; Patients; Pattern; Pediatric Hospitals; Peru; Pneumonia; Population; Pulmonary Pathology; Radiation; Resources; Respiratory Sounds; Respiratory Tract Infections; Roentgen Rays; Sensitivity and Specificity; Shapes; Signal Transduction; Site; Stethoscopes; Structure; System; Technology; Testing; Thoracic Radiography; Training; Transducers; Tuberculosis; Validation; Work; base; clinical Diagnosis; clinical practice; cohort; community center; computerized; computerized tools; cost; design; digital; experience; field study; frontier; improved; innovation; interest; invention; mortality; multidisciplinary; new technology; novel; patient population; point of care; prevent; respiratory; sensor; signal processing; sound; tool; treatment strategy

Project summary The use of chest auscultations to “listen” to and diagnose lung infections has been in practice since the invention of the stethoscope in the early 1800s. While it is a versatile tool that is universally used to complement clinical observation and other diagnosis methods (e.g. chest palpation, X-rays), it remains an outdated technology that has not evolved much beyond its early design. Its use is limited by subjectivity and inconsistency in interpreting chest sounds, inter-listener variability, need for advanced medical expertise as well as vulnerability to ambient noise that masks the presence of sound patterns of interest. In the current project, we propose to design a novel smart stethoscope to automate diagnosis of chest auscultations; especially for pediatric use. Over 2 million children die every year of acute lower respiratory infections (ALRI), the leading cause of childhood mortality worldwide. Our hypothesis is that if lung sounds are robustly acquired and analyzed, they are sufficiently informative to result in quantifiable improvements in detection accuracy of lung pathologies. By improving diagnosis capability using a low-cost technology, the proposed smart stethoscope will enhance resource and case management of ALRI, especially in impoverished settings that lack alternative diagnosis tools such as X-rays. This proposal focuses on two key components for improving efficacy of lung auscultation diagnosis: Aim 1: Designing the smart stethoscope technology. This effort takes a different engineering direction than devices currently on the market by employing novel transducer and microphone arrays in a layout that mitigates issues with ambient noise and signal stability. The expected outcome is to provide medical practitioners with a low-cost device that offers noise-control, signal amplification and stable recordings. We will test this technology at the Johns Hopkins Pediatric Emergency Hospital. Aim 2: Augmenting the smart stethoscope with computer-aided diagnosis. We propose adaptive signal processing methods for analyzing lung signals to enable differentiating normal from pathological cases. The expected outcome is to improve the specificity and sensitivity of lung diagnosis using computer-aided analyses, and help inform clinical decisions and ALRI case management. The efficacy of the algorithm is directly evaluated in a study at a children's hospital in Peru, using radiographic pneumonia as benchmark. The site is chosen as representative of applicability of the proposed device in a low-resource setting. The proposal is a multidisciplinary effort that draws upon the expertise of engineers and medical experts, with close interaction and ongoing validation in patient populations. Its overarching goal is to improve sensitivity and specificity of pulmonary diagnosis using auscultations. The overall outcome of this effort is a point-of-care technology that is effective, low-cost, and deployable for pulmonary-health monitoring in hospitals, clinics, low resource community centers, and potentially home-based monitoring.

项目摘要 从那以后，使用胸部听诊来“倾听”和诊断肺部感染。 听诊器在1800年代初发明。虽然它是一种通用的工具 补体临床观察和其他诊断方法（例如胸触诊，X射线），它仍然是一种 过时的技术并没有超出早期设计的发展。它的使用受主观性的限制 以及解释胸部声音，沿沿着速度的可变性，需要高级医学的不一致性 专业知识以及对环境噪声的脆弱性，掩盖了感兴趣的声音模式。 在当前的项目中，我们建议设计一种新颖的智能听诊器以自动化胸部的诊断 听诊；特别是用于小儿使用。每年有超过200万儿童死于急性下呼吸道 感染（ALRI），全世界儿童死亡率的主要原因。我们的假设是，如果肺部听起来 有强大的获取和分析，它们足够丰富，可以实现可量化的改进 肺病理的检测准确性。通过使用低成本技术提高诊断能力， 拟议的智能听诊器将增强ALRI的资源和案例管理，尤其是在 贫穷的设置缺乏替代诊断工具，例如X射线。 该提案重点是提高肺听诊诊断效率的两个关键组成部分： 目标1：设计智能听诊器技术。这项工作需要不同的工程方向 通过在布局中使用新颖的传感器和麦克风阵列，而不是当前在市场上的设备 这可以减轻环境噪声和信号稳定性的问题。预期的结果是提供医疗 具有低成本设备的从业者提供噪声控制，信号放大和稳定的记录。我们 将在约翰·霍普金斯小儿急诊医院测试这项技术。 AIM 2：使用计算机辅助诊断来增强智能听诊器。我们提出自适应信号 分析肺信号的处理方法使正常情况与病理病例区分开。 预期的结果是使用计算机AID提高肺诊断的特异性和敏感性 分析并帮助临床决策和ALRI案例管理。算法的效率是 在秘鲁儿童医院的一项研究中直接评估了射线照相肺炎作为基准。 选择该站点作为在低资源设置中代表所提出的设备的适用性。 该提案是一项多学科的工作，借鉴了工程师和医学的专业知识 专家，在患者人群中进行密切相互作用和持续验证。它的总体目标是 使用听诊提高肺诊断的敏感性和特异性。总体结果 努力是一种有效，低成本且可部署肺健康的技术点技术 在医院，诊所，资源较低的社区中心和潜在的家庭监控中进行监测。