Lung Diseases
Conditions
Keywords
lung sounds, adventitious lung sounds, abnormal lung sounds
Brief summary
The purpose of this research is to collect patient lung sounds in order to develop an artificial machine learning algorithm that can potentially tell a doctor if a patient is at risk of certain lung conditions.
Detailed description
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.
Interventions
DEVICEEko digital stethoscopes
Use of the Eko CORE 500 digital stethoscope and 3M Littmann CORE Digital Stethoscope to listen for and record lung sounds.
Sponsors
Eko Devices, Inc.
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* 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 (18 years or older)
Exclusion criteria
* Unable to have multiple recordings taken on chest and back (e.g. compromised mobility) * On mechanical ventilation
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Lung Sounds Collected from Number of Patients | Through study completion, an average of 8 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. |
Countries
United States
Outcome results
None listed