NCT07360522

Data Collection For Adventitious Lung Sounds Algorithm Using Eko Digital Devices in a Clinical Setting

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 750 (estimated)
• Sponsor: Eko Devices, Inc. · Industry
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

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.

Conditions

• Lung Diseases

Interventions

Type	Name	Description
DEVICE	Eko digital stethoscopes	Use of the Eko CORE 500 digital stethoscope and 3M Littmann CORE Digital Stethoscope to listen for and record lung sounds.

Timeline

• Start date: 2024-08-23
• Primary completion: 2025-07-21
• Completion: 2026-03-15
• First posted: 2026-01-22
• Last updated: 2026-01-22

Locations

1 site across 1 country: United States