Data Collection Using Eko Digital Devices in a Clinical Setting
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 200
- 试验地点
- 3
- 主要终点
- Primary Objective: Collection of Lung Sound Recordings to Explore Machine Learning Algorithm for Classifying Adventitious Lung Sounds
研究概览
简要总结
The main objectives of the study are to: train and validate binary classifiers for wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough, as well as determine correspondence between type/location of adventitious lung sound and type of pulmonary condition.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Patients suspected or diagnosed lower respiratory condition OR Presence of wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough discovered during routine auscultation
- •Normal patients with no adventitious lung sounds
- •Adults patients, over 18 years old
- •Able to provide verbal consent
排除标准
- •Patients unable to have multiple recordings taken on chest and back (e.g. compromised mobility)
- •Patients on mechanical ventilation
- •Patients unwilling or unable to provide informed consent
结局指标
主要结局
Primary Objective: Collection of Lung Sound Recordings to Explore Machine Learning Algorithm for Classifying Adventitious Lung Sounds
时间窗: 14-15 months
The primary objective of this study is to collect normal and abnormal lung sounds of up to 750 patients per study site, by having clinicians use the Eko CORE and/or Eko CORE 500 device(s) in real clinical settings, as part of standard of care clinical practice which will then be used to explore an ML algorithm for classifiers for wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough.
次要结局
未报告次要终点