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NCT07227376Recruiting

Data Collection Using Eko Digital Devices in a Clinical Setting

Eko Devices, Inc.

Start Date

9/14/2025

Completion Date

11/30/2027

Summary

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.

Eligibility Criteria

Age Range: No minimum to No maximum

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

Interventions

DEVICE

Eko digital stethoscopes

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Conditions

Lung Diseases

Locations

Nemours Children's Health

Jacksonville, Florida 32256

United States

Jefferson Einstein Philadelphia Hospital

Philadelphia, Pennsylvania 19141

United States