RecruitingNCT07542509

Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]

Sponsor: IRCCS Azienda Ospedaliero-Universitaria di Bologna

Trial Details

Condition	Cardiac Disease, Auscultation of Heart, Machine Learning
Status	Recruiting
Participants	1,000
Est. Completion	1 Feb 2026
Last Updated	21 Apr 2026

Primary Outcome

Binary classifier for normal versus abnormal cardiac sounds in newborns

Eligibility Criteria

Inclusion Criteria

Inclusion criteria: * Age \< 30 days * Signed informed consent obtained from parent(s) or representative(s) Exclusion criteria: * Inability to acquire a diagnostic echocardiogram * Weight less than 1.5Kg

Trial Sites

Countries

Italy

View Registry Entry

Related Analysis

DI-SOUND Study: Digital Stethoscope AI Classifier for Neonatal Cardiac Screening — Proof-of-Concept Trial Recruiting 1,000 Newborns

Italian researchers are developing a digital classifier using machine learning to improve neonatal cardiac screening through automated analysis of heart sounds recorded with commercial digital stethoscopes. The DI-SOUND study aims to recruit 1,000 newborns to create a binary classification system distinguishing normal from abnormal cardiac sounds.

Read full article →

This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service