Can Smartphones Listen to Your Heart? A Performance Study on Detecting Inborn Heart Diseases in Your Heart Sounds

NCT07376785 | Not Yet Recruiting

• 2 other identifiers: AUSC-CHDUW 25-328
• Study Type: observational
• Target: 220
• Locations: 1 country
• Sites: 2

Brief Summary

This observational study aims to assess the performance of the software called ausculto™. ausculto™ is a collection of computer algorithms that intend to analyse heart sounds recorded from the built-in microphone of a smartphone for abnormal sounds. Participants will have their heart sounds recorded during their normal attendance at the hospitals after consenting to participate in this study. Researchers will manually annotate the recorded heart sounds to create a database for use in future training and testing of artificial intelligence (AI) intended for medical uses.

Conditions

Congenital Heart Disease (CHD)

Keywords

Smartphone; Mobile Applications; Telemedicine; Heart Defects, Congenital; Phonocardiography; Heart Sounds; Heart Murmurs; Heart Auscultation

Outcome Measures

Primary Outcomes (1)

• Differentiation of Murmurs Associated with Congenital Heart Disease from Normal Heart Sounds and Innocent Murmurs in Heart Sound Recordings

  Identification of murmurs associated with congenital heart disease from normal heart sounds and innocent murmurs in recorded heart sounds (phonocardiogram \[PCG\]), measured in the form of sensitivity and specificity.

  Day 0

Secondary Outcomes (2)

• Differentiation of Murmurs Associated with Congenital Heart Disease from Normal Heart Sounds and Innocent Murmurs in Heart Sound Recordings

  Day 0

• Number of Adverse Events Reported by Participants or Child Participants' Parent/Legal Guardian During and at the End of Heart Sound Recording That are Determined to be Caused by This Study

  Day 0

Study Arms (2)

Abnormal Echocardiography Diagnosis

Participants with abnormal echocardiography diagnosis within three years.

Diagnostic Test: Computer algorithms

Normal Echocardiography Diagnosis

Participants with normal echocardiography diagnosis within three years.

Diagnostic Test: Computer algorithms

Interventions

Computer algorithms DIAGNOSTIC_TEST

Collection of lightweight computer algorithms called ausculto™ that is designed to perform real-time heart sound analysis to detect heart murmur.

Also known as: ausculto™, Vitogram™

Abnormal Echocardiography Diagnosis; Normal Echocardiography Diagnosis

Eligibility Criteria

• Age: 6 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Paediatric and adult hospital patients who have undergone echocardiography within 3 years.

You may qualify if:

• Age: ≥ 6 years
• Have undergone echocardiography within 3 years at their normal non-research hospital attendance

You may not qualify if:

• Implanted active medical devices in the torso, such as pacemakers and defibrillators
• Have purely non-congenital valvular heart disease

Sponsors & Collaborators

• The University of Hong Kong: lead
• Laboratory of Data Discovery for Health: collaborator

Study Sites (2)

Queen Mary Hospital

Hong Kong, Hong Kong

Location

Hong Kong Children's Hospital

Kowloon Bay, Hong Kong

Location

MeSH Terms

Conditions

Heart Defects, Congenital; Heart Murmurs

Condition Hierarchy (Ancestors)

Cardiovascular Abnormalities; Cardiovascular Diseases; Heart Diseases; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Chun Ka Wong

CONTACT

852 2255 6233; wongeck@hku.hk

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Assistant Professor

Study Record Dates

• First Submitted: January 21, 2026
• First Posted: January 29, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): November 1, 2026
• Last Updated: April 29, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

HK (2)