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COPD的成人。肺部疾病 给医疗系统、个人和政府带来了沉重的负担。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（LRI） 第三和第四是2016年的主要死亡原因，每年各夺去300万人的生命。LRI计数 14.9%的儿童死亡是由这一疾病引起的，这使其成为早产后婴儿死亡的主要原因。哮喘a 与慢性阻塞性肺病一样，这种疾病也是儿童的主要慢性疾病， 全世界有2.35亿人患有这种疾病，2015年有超过38万人死于这种疾病。哮喘 和慢性阻塞性肺病去年分别给美国造成了大约560亿美元和720亿美元的损失。负担 这些疾病和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增加，预计呼吸道疾病将增加155%， 而预计到2030年，肺科专家将严重短缺，预计将下降7%。 我们认为，一个长期的监测解决方案，可以在家里使用的未经培训的病人，或 可以检测和监测患者气道炎症的严重程度， 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，将使慢性病患者能够获得力量，同时也 减少急诊次数和再入院率。我们发现一些挑战 在非传统临床环境中考虑长期听诊监测解决方案时存在：（1） 不可预测的环境噪声，（2）需要医学专业知识来解释肺音，（3）主观性， 分析，和（4）难以使用和放置听诊器。研究小组开发了一种智能听诊器 最初是供低资源国家的社区卫生工作者使用的， 小儿患者之间的爆裂音和喘息，克服了许多这些挑战。这款智能 听诊器通过包括（1）自适应噪声抑制来解决上述所有挑战， 客观和主观地证明在所有类型的噪音环境中比传统或其他噪音环境具有上级性能 电子听诊器，（2）机载分析算法，可以检测小儿的爆裂声和喘息 具有与专家匹配的准确度的患者，以及（3）均匀的拾取表面，其去除了 需要准确放置器械以获得准确记录。 在这个项目中，我们将验证该设备可以正确地使用的家长与哮喘儿童， 可以进行精确的记录，其在质量上类似于由医疗人员进行的记录。 专业.同时，我们将利用患者反馈来迭代设备和移动的应用程序设计 创造一个病人在家里使用时感到舒适的版本。一旦设备和应用程序已经 经过第一阶段的验证，我们计划直接进入第二阶段，在第二阶段，该设备将进入第二阶段 调查将包括一项首次纵向研究，来自儿童患者的父母， 录音在家里。然后，这些数据将用于开发算法，以确定肺 可靠的严重性，具有可随时间跟踪和预测的指标。在本临床研究的同时 和算法开发，将记录成年COPD患者，以扩展 超越小儿科的设备