Efficacy Comparison Between Independent Auscultation by Primary Care Physicians and AI-Assisted Auscultation in Large-scale Screening for Congenital Heart Disease: A Cluster Randomized Controlled Trial in China

NCT06791109 | Recruiting

• 2 other identifiers: XHEC-C-2025-013-1INV-072724
• Study Type: interventional
• Target: 28,833
• Locations: 1 country
• Sites: 1

Brief Summary

This study plans to conduct clinical validation of the model in real clinical settings, comparing it with primary care physicians and specialist physicians to ensure the model's practicality. Through continuous optimization and practice, the study aims to use AI-assisted heart sound auscultation to empower the auscultation capabilities of primary care obstetricians, pediatricians, and non-cardiovascular specialists nationwide. This will not only reduce the missed diagnosis rate and improve the detection rate of existing CHD screenings, but also expand the coverage of current CHD screening networks, incorporating newborns, infants, preschool children, children, and adolescents aged 0-18 years into the screening scope. The study aims to establish a new benchmark in child health management by providing feasible and cost-effective child health management solutions for other developing countries, contributing to global efforts for the health of children.

Conditions

Congenital Heart Disease (CHD); Screening Tool; Artificial Intelligence (AI); Cluster Randomized Trial; Auscultation for Clinical Evaluation

Outcome Measures

Primary Outcomes (1)

• Sensitivity of auscultation in identifying CHD between independent auscultation by primary care physicians and AI-assisted auscultation by primary care physicians

  From enrollment to the end of treatment at 6 months

Secondary Outcomes (4)

• Specificity, accuracy, and false negatives of Auscultation in CHD Detection: Primary Care Physicians' Independent Auscultation & AI-assisted Primary Healthcare Physicians' Auscultation

  From enrollment to the end of treatment at 6 months

• The rate of diagnostic revisions by physicians, the proportions of correct and incorrect changes

  From enrollment to the end of treatment at 6 months

• Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians/Experienced Cardiologists' Independent Auscultation & AI Model

  From enrollment to the end of treatment at 6 months

• Sensitivity, specificity, accuracy, and false negatives of Auscultation in CHD Detection: Specialist Physicians' Independent Auscultation & AI-assisted Primary Healthcare Physicians' Auscultation

  From enrollment to the end of treatment at 6 months

Other Outcomes (2)

• Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physician & Experienced Cardiologist

  From enrollment to the end of treatment at 6 months

• Incidence rates of CHD in children across different regions of Qinghai Province

  From enrollment to the end of treatment at 6 months

Study Arms (2)

Primary care physicians

ACTIVE COMPARATOR

Diagnostic Test: Primary care physicians

AI + Primary care physicians

EXPERIMENTAL

Diagnostic Test: AI + Primary care physicians

Interventions

Primary care physicians DIAGNOSTIC_TEST

For participants in Group A, a nonblinded independent staff member will first collect medical history, followed by sequential auscultation and CHD assessment by a specialist physician and a primary care physician, with an echocardiogram performed last.

Primary care physicians

AI + Primary care physicians DIAGNOSTIC_TEST

For participants in Group B, after medical history collection, both a specialist physician and a primary care physician will perform auscultation and CHD assessment. Subsequently, the primary care physician will use an electronic stethoscope to collect heart sound data according to the protocol and upload the recordings to a cloud platform. The AI model will analyze the data on the cloud platform and provide a diagnostic result within 5-10 seconds for the primary care physician's reference. The primary care physician may reassess the findings based on the AI model's feedback, and the participant will then undergo an echocardiogram.

AI + Primary care physicians

Eligibility Criteria

• Age: Up to 18 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)

You may qualify if:

• The school is of the type: kindergarten, primary school, junior high school, or senior high school.
• The school has medical screening facilities and conditions that can support AI-assisted screening.
• The area where the school is located has at least one primary healthcare institution willing to participate in this trial.
• The school's management and teaching staff are willing to participate in the study and can cooperate to complete the related screening and data collection work.

You may not qualify if:

• More than half of the students in the school refuse to participate in the trial.
• Schools that are unable to complete the study due to severe limitations in geographical location and transportation conditions.
• Schools lacking medical screening facilities and conditions necessary for the implementation of screening.
• Areas where there are no primary healthcare institutions willing to participate in this trial.
• Schools whose management and teaching staff refuse to participate in the study or are unable to cooperate in completing the related screening and data collection work.
• Children aged between 0 and 18 years, regardless of gender.
• Children who agree to undergo echocardiography to determine the presence of congenital heart disease.
• Individuals who voluntarily participate in this study and sign the informed consent form.
• Individuals over the age of 18.
• Children who are unable to complete echocardiography or do not cooperate with auscultation.
• Participants who are unable to provide informed consent or are unwilling to comply with the study requirements to provide medical data for further analysis and research.
• Participants who have received interventional or surgical treatment for CHD.

Sponsors & Collaborators

• Kun Sun: lead
• Bill and Melinda Gates Foundation: collaborator

Study Sites (1)

Qinghai Provincial Women and Children's Hospital

Xining, Qinghai, 810007, China

RECRUITING

MeSH Terms

Conditions

Heart Defects, Congenital

Condition Hierarchy (Ancestors)

Cardiovascular Abnormalities; Cardiovascular Diseases; Heart Diseases; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Central Study Contacts

Kun Sun, Phd

CONTACT

021-13601846338; drsunkun@xinhuamed.com.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor of Department of Pediatric Cardiology

Study Record Dates

• First Submitted: January 18, 2025
• First Posted: January 24, 2025
• Study Start: September 22, 2025
• Primary Completion: December 31, 2025
• Study Completion: February 22, 2026
• Last Updated: September 26, 2025

Record last verified: 2025-09

Locations

CN (1)