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: 10274773
• 负责人: Ilene Joy Busch-Vishniac
• 金额: $ 114.83万
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10274773
• 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 Pulmonary 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; 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; Persons; Phase; Physicians; Pollution; Population; Premature Birth; Process; Pulmonary Challenge; Pulmonology; Questionnaires; Research; Research Personnel; Resources; Respiratory Disease; 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; home test; 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.

项目概要 该项目的目标是进一步开发现有的智能听诊器，以便能够 在家中监测患有哮喘的儿科患者以及患有慢性阻塞性肺病的成人。 给世界卫生组织、个人和政府带来严重负担。 （世界卫生组织）发现慢性阻塞性肺疾病（COPD）和下呼吸道感染（LRI）排名 2016 年，第三和第四位死亡原因分别占 300 万人的生命。 占儿科死亡人数的 14.9%，使其成为早产后婴儿死亡的主要原因。 与慢性阻塞性肺病一样，这种疾病无法治愈，也是儿童中主要的慢性疾病，据估计 全球有 2.35 亿人患有这种疾病，2015 年有超过 38 万人死于这种疾病。 去年，美国因慢性阻塞性肺病和慢性阻塞性肺病分别花费了约 560 亿美元和 720 亿美元。 这些疾病的数量以及人口之间的健康差距在未来十年只会变得更糟， 由于人口老龄化和污染加剧，呼吸系统疾病预计将增加 155%， 而肺科专家预计将出现严重短缺，预计到 2030 年将减少 7%。 我们推断，未经培训的患者可以在家中使用长期监测解决方案，或者 患者家属可以检测和监测患者空气炎症的严重程度，提供 深入了解症状恶化或改善的原因，推送量身定制的教育内容并指导患者 在情况变得严重之前进行医疗跟踪，将赋予慢性病患者权力，同时也 减少去急诊室的次数和再入院率 我们发现存在一些挑战。 在非传统临床环境中考虑长期听诊监测解决方案时存在以下问题：(1) 不可预测的环境噪音，(2) 需要医学专业知识来解释肺音，(3) 主观性 分析，（4）听诊器的使用和放置困难 研究团队开发了智能听诊器。 最初旨在供资源匮乏国家的社区卫生工作者使用，以区分 在患有爆裂声和喘息的儿科患者之间进行治疗，克服了许多这些挑战。 听诊器通过包括 (1) 自适应噪声抑制来解决上述所有挑战 客观和主观证明在所有类型的噪声环境中均优于传统或其他 电子听诊器，(2) 机载分析算法，可检测儿科爆裂音和喘息音 患者的准确度可与专家相匹配，以及 (3) 统一的拾取表面，可去除 要求设备精确放置以获得准确的录音。 在这个项目中，我们将验证该设备是否可以被哮喘儿童的家长正确使用，以及 可以进行准确的记录，其质量与医疗记录的记录相似 同时，我们将利用患者的反馈来迭代设备和移动应用程序的设计。 创建一个患者在家中可以舒适使用的版本。 在第一阶段验证后，我们计划直接进入第二阶段，该设备将进入第二阶段 的调查，其中包括对每天服用药物的儿科患者父母进行的首次纵向研究 然后，这些数据将用于开发确定肺部的算法。 声音的严重性以及可以随着时间的推移进行跟踪和预测的指标。 和算法开发，将对成年慢性阻塞性肺病患者进行记录，以扩大该系统的可用性 超出儿科范围的设备。