Lung Diseases
Conditions
Keywords
lung sounds, adventitious lung sounds, abnormal lung sounds
Brief summary
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.
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
* 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
Exclusion criteria
* 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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Primary Objective: Collection of Lung Sound Recordings to Explore Machine Learning Algorithm for Classifying Adventitious Lung Sounds | 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. |
Countries
India
Outcome results
None listed