Feelix @ Home: Testing and optimization of a smart stethoscope for home use to monitor changes in lung status of individuals with chronic conditions

Feelix @ Home：测试和优化家用智能听诊器，用于监测慢性病患者肺部状况的变化

• 批准号: 9909859
• 负责人: Ilene Joy Busch-Vishniac
• 金额: $ 23.65万
• 依托单位: SONAVI LABS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9909859
• 关键词: Accident and Emergency department; Acoustics; Acute; Address; Adult; Agreement; Algorithmic Analysis; Algorithms; Asthma; Auditory; Auscultation; Businesses; Caregivers; Categories; Cause of Death; Censuses; Cessation of life; Child; Childhood; Chronic; Chronic Disease; Chronic Obstructive Airway Disease; Clinical; Clinical Data; Clinical Research; Community Health Aides; Cost of Illness; Country; Crackles; Data; Data Collection; Data Set; Detection; Development; Devices; Disease; Dropout; Electronic Health Record; Electronics; Emergency department visit; Environment; Family; Family member; Feedback; Focus Groups; Future; Goals; Government; Health; Healthcare; Healthcare Systems; Home environment; Hospitalization; Hospitals; Housing; Human Resources; Individual; Infant Mortality; Intelligence; Intervention; Investigation; Letters; Link; Longitudinal Studies; Lower Respiratory Tract Infection; Lung; Lung diseases; Medical; Medical Staff; Modification; Monitor; Noise; Outcome; Parents; Participant; Patient Monitoring; Patient Recruitments; Patient Transfer; Patient-Focused Outcomes; Patients; Pediatrics; Phase; Physicians; Pollution; Population; Premature Birth; Process; Pulmonology; Questionnaires; Research; Research Personnel; Resources; Respiratory Sounds; Severities; Signal Transduction; Site; Small Business Technology Transfer Research; Specialist; Stethoscopes; Surface; System; Technology; Technology Transfer; Test Result; Testing; Time; Training; United States; Universities; Untrained Personnel; Wheezing; Work; World Health Organization; aging population; airway inflammation; algorithm development; base; burden of illness; clinical research site; clinically relevant; commercialization; computer aided detection; design; digital; efficacy trial; health disparity; improved; individual patient; insight; instrument; intelligent algorithm; mobile application; monitoring device; pediatric patients; readmission rates; recruit; research clinical testing; signal processing; sound; supervised learning; symptomatic improvement; trend; usability

Project Summary The goal of this project is to further develop an existing smart stethoscope in order to be capable of monitoring pediatric patients at home who suffer from asthma as well as adults with COPD. Lung diseases impose a serious burden on healthcare systems, individuals and governments. The World Health Organization (WHO) found that chronic obstructive pulmonary disease (COPD) and lower respiratory infections (LRIs) ranked third and fourth as the leading causes of death in 2016, each claiming 3 million lives annually. LRIs accounted for 14.9% of pediatric deaths, making it the leading cause of infant mortality after pre-term birth. Asthma—a condition for which, like COPD, there is no cure—is also the leading chronic disease in children and an estimated 235 million people suffer from the disease worldwide, with over 380,000 deaths from the disease in 2015. Asthma and COPD costed the United States approximately $56 billion and $72 billion last year, respectively. The burden of these diseases and the health disparities across populations is only slated to get worse in the coming decade, as respiratory diseases are expected to increase by 155% due to an aging population and increased pollution, while there is expected to a large shortage of pulmonary specialists, with an expect 7% decline by 2030. We reasoned that a long-term monitoring solution that can be used in the home by untrained patients, or family members of patients, that could detect and monitor severity of airway inflammation in patients, provide insight into reasons for worsening or improved symptoms, push tailored educational content, and direct patients to medical follow before the situation becomes acute, would empower patients with chronic conditions while also reducing trips to emergency departments and readmission rates to hospitals. We find that several challenges exist when considering long term auscultatory monitoring solutions in non-traditional clinical settings: (1) unpredictable ambient noise, (2) the need for medical expertise to interpret lung sounds, (3) subjectivity in the analysis, and (4) difficulty using and placing the stethoscope. The research team developed a smart stethoscope that was originally intended for use in low-resource countries by community health workers to differentiate between pediatric patients with crackles and wheezes that overcomes many of these challenges. This smart stethoscope address all the challenges above by including (1) adaptive noise suppression that has been objectively and subjectively proven to be superior in all types of noise environments than traditional or other electronic stethoscopes, (2) on-board analysis algorithms that can detect crackles and wheezes in pediatric patients with an accuracy that matches that of a specialist, and (3) a uniform pickup surface that removes the requirement for exact placement of the device to get an accurate recording. In this project, we will validate that the device can be correctly used by parents of children with asthma and accurate recordings can be taken that are similar in quality to those that would be taken by a medical professional. Simultaneously, we will be using patient feedback to iterate on the device and mobile app design to create a version that patients are comfortable using in their home. Once the device and app have been validated in Phase I, we plan to move into directly into Phase II where the device will enter in a second phase of investigation that will include a first-time longitudinal study from parents of pediatric patients taking daily recordings in their home. This data will then be used for the development of algorithms to determine lung sound severities with metrics that can be tracked and predicted over time. In parallel to the this clinical study and algorithm development, recordings will be taken of adult patients with COPD to expand the usability of the device beyond pediatrics.

项目摘要 该项目的目的是进一步开发现有的智能听诊器，以便能够 监测患有哮喘和患有COPD成年人的家里的小儿患者。肺部疾病 对医疗保健系统，个人和政府施加严重的伯宁。世界卫生组织 （WHO）发现慢性阻塞性肺疾病（COPD）和下呼吸道感染（LRIS）排名 第三和第四，是2016年的主要死亡原因，每年都有300万人的生命。 LRIS占了 小儿死亡的14.9％，使其成为前期出生后婴儿死亡率的主要原因。哮喘 - 一个 像COPD一样，无法治愈的状况，也是儿童的主要慢性疾病，估计 2.35亿人在全球疾病中遭受这种疾病，2015年死亡380,000人。哮喘。 去年，COPD分别花费了美国约560亿美元和720亿美元。伯恩 在这些疾病和人群之间的健康分配中，只有在未来十年中变得更糟 由于人口老龄化和污染增加，预计呼吸系统疾病将增加155％，所以 虽然预计会有很大的肺部专家短缺，但预计到2030年下降了7％。 我们认为，未经训练的患者可以在家庭中使用的长期监测解决方案或 可以检测和监测患者气道炎症严重程度的患者家庭成员 深入了解担心或改善症状的原因，推动量身定制的教育内容并指导患者 在情况急性之前进行医学跟随 降低前往急诊室的旅行，再到医院的再入院率。我们发现有几个挑战 在非传统临床环境中考虑长期听诊监测解决方案时存在：（1） 不可预测的环境噪声，（2）需要医学专业知识来解释肺部声音，（3） 分析，以及（4）难以使用和放置听诊器。研究小组开发了一个智能的听诊器 这最初是为了在低资源国家使用社区卫生工作者来区分 在克服裂纹和轮毂的小儿患者之间，克服了许多挑战。这个聪明 听诊器通过包括（1）已有的自适应噪声来应对上述所有挑战 客观和主观证明在所有类型的噪声环境中都比传统或其他噪声环境优越 电子听诊器，（2）可以检测小儿裂纹和轮子的板载分析算法 具有与专家相匹配的精度的患者，以及（3）均匀的拾音器表面，可去除 确切放置设备以获得准确记录的要求。 在这个项目中，我们将验证该设备可以由哮喘和儿童的父母正确使用 可以拍摄准确的记录，质量与医疗相似的质量相似 专业的。同时，我们将使用患者的反馈来迭代设备和移动应用设计 创建一个患者在家中使用的版本。一旦设备和应用程序 在第I阶段验证，我们计划直接进入II阶段，该设备将在第二阶段进入 投资，其中包括小儿患者父母的首次纵向研究 他们家中的录音。然后，这些数据将用于开发算法以确定肺 可以随着时间的推移跟踪和预测的指标的声音严重性。与这项临床研究并行 和算法开发，将记录COPD的成年患者，以扩大 儿科以外的设备。