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
- 250
- 试验地点
- 4
- 主要终点
- Primary Objective
研究概览
简要总结
The purpose of this research is to prospectively train and validate an artificial intelligence machine learning (ML) algorithm to detect the presence of adventitious lung sounds in adults. Clinicians will use the Eko CORE and/or Eko CORE 500 device(s) in real clinical settings to collect normal and abnormal lung sounds, 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, as well as determine any correspondences between the type and/or location of adventitious lung sounds and the type of pulmonary conditions as reported by clinicians.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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 and pediatric patients (as available)
排除标准
- •Unable to have multiple recordings taken on chest and back (e.g. compromised mobility)
- •On mechanical ventilation
结局指标
主要结局
Primary Objective
时间窗: Through study completion, an average of 8-9 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, as well as determine any correspondences between the type and/or location of adventitious lung sounds and the type of pulmonary conditions as reported by clinicians.
次要结局
未报告次要终点