Data Collection Using Eko Digital Devices in a Clinical Setting

Purpose

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.

Condition

  • Lung Diseases

Eligibility

Eligible Ages
All ages
Eligible Sex
All
Accepts Healthy Volunteers
No

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

Study Design

Phase
Study Type
Observational
Observational Model
Cohort
Time Perspective
Prospective

Recruiting Locations

Nemours Children's Health
Jacksonville, Florida 32256
Contact:
Jackrin Walsh Clinical Research Associate
8443563384
jackrin.walsh@ekohealth.com

Jefferson Einstein Philadelphia Hospital
Philadelphia, Pennsylvania 19141

More Details

Status
Recruiting
Sponsor
Eko Devices, Inc.

Study Contact

Clinical Research Associate
8443563384
jackrin.walsh@ekohealth.com