Improving the Differential Diagnosis of Pneumonia and Congestive Heart Failure Us

改善肺炎和充血性心力衰竭的鉴别诊断

• 批准号: 7434091
• 负责人: RAYMOND L H MURPHY
• 金额: $ 15万
• 依托单位: STETHOGRAPHICS, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7434091
• 关键词: Acoustics; Algorithms; Antibiotics; Area; Asthma; Biological Neural Networks; Breathing; Categories; Chronic Obstructive Airway Disease; Classification; Computer software; Condition; Congenital Heart Defects; Congestive Heart Failure; Count; Crackles; Data; Databases; Development; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Diuretics; Electronics; Equipment; Expert Systems; Goals; Grant; Heart Sounds; Heart failure; Heart murmur; Home environment; Hospital Nursing; Intensive Care Units; Lead; Legal patent; Lung; Lung diseases; Medical; Methods; Nurses; Nursing Homes; Oils; Patient Care; Patient Monitoring; Patients; Pattern Recognition; Personal Digital Assistant; Phase; Physicians' Offices; Pneumonia; Public Health; Pulmonary Fibrosis; Pulmonary Heart Disease; Rate; Research; Rhonchus; Risk; Shipping; Ships; Source; Specificity; Stethoscopes; System; Techniques; Technology; Telemedicine; Uncertainty; United States; United States Food and Drug Administration; Visiting Nurse; Wheezing; base; clinically significant; computerized; improved; interstitial; noninvasive diagnosis; patient home care; sensor; sound; telemetering; transmission process

DESCRIPTION (provided by applicant): Stethographics has developed automated lung and heart sound products, based on 3 granted U.S. patents and 2 FDA approvals. Our Pocket PC based system gathers sounds via a contact sensor in a simple and practical way. The system automatically detects and quantifies crackles, wheezes, rhonchi, squawks and heart murmurs. 3M Littmann, the world} s largest stethoscope company, bundles all E4000 electronic stethoscopes with our Sound Analysis Software. We propose a research plan that will lead to development of a} smart} stethoscope. In addition to extracting sound features like crackle count, wheeze rate, and heart murmur grade, the incorporated neural network algorithms will provide a probable cause of these abnormal sounds such as pneumonia, congestive heart failure, or heart abnormality. We expect the smart stethoscope to find its applications in many settings: in physician's offices, hospitals, nursing homes - essentially everywhere the stethoscope is used. In addition, new areas of exploitation include settings where doctoral expertise or stationary medical equipment is not always available, and nurse is the main source of medical help: on the ships, oil rigs, embassies and home care by visiting nurses. The diagnostic information provided by the smart stethoscope can be used locally or telemetered. We have initiated this research by tackling two common illnesses: pneumonia (PN) and congestive heart failure (CHF). It is estimated that 5 million people in the United States have CHF. Although in many instances the diagnosis of these conditions is easily made, it is not uncommon, particularly in the Intensive Care Unit setting, for it to be unclear as to which illness a patient has. In cases of doubt the patient is often treated for both. Yet diuretics are likely not good for patients with pneumonia in the absence of coexisting heart failure and it is not good practice to subject patients to the risk of antibiotics unnecessarily. Our preliminary results in 151 patients with 2 or more crackles per breath (CHF=70; PN=81) indicate that the crackles differ significantly in these two conditions. Classification algorithms based on crackles features were able to separate the two disorders with a sensitivity of 0.91 and specificity of 0.82. In Phase I we plan to retrospectively study the database of over 1,000 patients using pattern recognition methods in order to develop the expert system that can differentiate PN, CHF, interstitial pulmonary fibrosis (IPF), and normal patients. In Phase II we will expand the system to include diagnosis of asthma, COPD, and cardiac murmurs. In Phase III we will incorporate the expert system into a smart stethoscope. PUBLIC HEALTH RELEVANCE: This research is expected to provide new medical diagnostic software that can be incorporated into a smart stethoscope. The use of the smart stethoscope will be particularly relevant in settings where doctoral expertise or stationary medical equipment is not always available and nurse is the main source of medical help. Automated diagnostics with the smart stethoscope can simplify and improve care for patients in nursing homes, especially by detecting early signs of pneumonia and home monitoring of patients with cardiopulmonary disorders.

描述（由申请人提供）：Stethographics基于3项授权的美国专利和2项FDA批准开发了自动化肺音和心音产品。我们的基于Pocket PC的系统通过接触式传感器以简单实用的方式收集声音。该系统自动检测和量化爆裂声，喘息声，干罗音，尖叫声和心脏杂音。世界上最大的听诊器公司3 M Littmann将所有E4000电子听诊器与我们的声音分析软件捆绑在一起。我们提出了一个研究计划，将导致发展的一个智能听诊器。除了提取声音特征，如爆裂声计数，喘息率和心脏杂音等级外，结合的神经网络算法将提供这些异常声音的可能原因，如肺炎，充血性心力衰竭或心脏异常。我们希望智能听诊器能在许多环境中找到应用：医生的办公室，医院，疗养院-基本上在使用听诊器的任何地方。此外，新的开发领域包括博士专业知识或固定医疗设备并不总是可用的环境，护士是医疗帮助的主要来源：在船上，石油钻井平台，大使馆和上门护士的家庭护理。智能听诊器提供的诊断信息可以在本地使用或遥测。我们通过解决两种常见疾病开始了这项研究：肺炎（PN）和充血性心力衰竭（CHF）。据估计，美国有500万人患有CHF。虽然在许多情况下，这些疾病的诊断很容易，但并不罕见，特别是在重症监护室环境中，不清楚患者患有何种疾病。在怀疑的情况下，患者通常会接受两种治疗。然而，利尿剂可能对没有合并心力衰竭的肺炎患者没有好处，并且不必要地使患者承受抗生素的风险也不是一个好的做法。我们对151例每次呼吸有2次或2次以上湿罗音的患者（CHF=70; PN=81）的初步结果表明，这两种情况下的湿罗音有显著差异。基于爆裂音特征的分类算法能够区分这两种疾病，灵敏度为0.91，特异性为0.82。在第一阶段，我们计划使用模式识别方法回顾性研究超过1,000例患者的数据库，以开发能够区分PN、CHF、间质性肺纤维化（IPF）和正常患者的专家系统。在第二阶段，我们将扩展该系统，包括哮喘，COPD和心脏杂音的诊断。在第三阶段，我们将把专家系统集成到智能听诊器中。公共卫生相关性：这项研究有望提供新的医疗诊断软件，可以集成到智能听诊器中。在医生专业知识或固定医疗设备并不总是可用并且护士是医疗帮助的主要来源的情况下，智能听诊器的使用将特别相关。使用智能听诊器进行自动诊断可以简化和改善护理院患者的护理，特别是通过检测肺炎的早期症状和心肺疾病患者的家庭监测。