Recording lung sounds with Eko digital stethoscopes in clinic
Official title: Data Collection Using Eko Digital Devices in a Clinical Setting
Run by Eko Devices, Inc. · NCT07227376 · Data updated 2026-11-12
This project will test an AI that listens to lung sounds recorded with Eko digital stethoscopes to find abnormal sounds in people with or without lung disease.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 250 (estimated) |
| Sponsor | Eko Devices, Inc. Industry-sponsored |
| Locations | 2 sites (Jacksonville, Florida and 1 other locations) |
| Status | Recruiting |
| Started | September 14, 2025 |
| Main results expected | April 1, 2027 |
| Study ends | November 30, 2027 |
| Registry record updated | May 26, 2026 |
| Trial ID | NCT07227376 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
People who can tolerate multiple chest and back recordings, including adults and children with suspected or diagnosed lower respiratory conditions and healthy individuals without adventitious sounds, are ideal candidates.
✕Probably not a fit
People who are mechanically ventilated or unable to have multiple chest/back recordings would not be eligible and are unlikely to benefit from this project.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
In routine clinic visits, clinicians will use Eko CORE and/or CORE 500 digital stethoscopes to record normal and abnormal lung sounds from the chest and back. Recorded sounds will be labeled with clinician-reported findings and clinical context and then used to train and validate a machine learning algorithm to classify wheeze, coarse crackle, fine crackle, rhonchus, stridor, rales, and cough. The study will include adult and pediatric patients as available and capture recordings during standard-of-care encounters. Patients who cannot tolerate multiple recordings or who are on mechanical ventilation are excluded.
Why it matters
Potential benefit: If successful, the AI could help clinicians detect and classify abnormal lung sounds more quickly and consistently, potentially speeding diagnosis and treatment.
How similar studies have gone: Similar AI approaches to automated lung sound analysis have shown promising early results in research settings, but large-scale clinical validation is still limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
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 and pediatric patients (as available) Exclusion Criteria: * Unable to have multiple recordings taken on chest and back (e.g. compromised mobility) * On mechanical ventilation
Where it's running
-
Nemours Children's Health
Jacksonville, Florida, United States Recruiting -
Jefferson Einstein Philadelphia Hospital
Philadelphia, Pennsylvania, United States Recruiting
Study contacts
- Study coordinator: Clinical Research Associate
- Email: jackrin.walsh@ekohealth.com
- Phone: 8443563384
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.