Efficacy Comparison Between Primary Care Physicians' Independent Auscultation and AI-assisted Auscultation for Congenital Heart Disease Screening in Patient-enriched Populations: a Randomized Controlled Trial

NCT06791096 | Recruiting

• 2 other identifiers: XHEC-C-2025-012-1INV-072724
• Study Type: interventional
• Target: 420
• Locations: 1 country
• Sites: 2

Brief Summary

In recent years, the application of artificial intelligence (AI) in the healthcare domain has witnessed a significant surge, with deep learning emerging as a potent force in the medical field. Deep learning algorithms possess the remarkable ability to automatically extract intricate features and patterns, thereby facilitating highly accurate heart sound recognition. Drawing on this technological advancement, Professor Sun Kun and his research team from Xinhua Hospital, in collaboration with numerous centers spanning across China, have been diligently investigating the development and application of AI-assisted heart sound recognition for congenital heart disease (CHD) screening. Utilizing electronic stethoscopes to meticulously collect heart sounds, and harnessing AI algorithms to analyze extensive datasets comprising heart sounds from both children diagnosed with CHD and those who are healthy, the system has been trained to adeptly differentiate between normal and pathological murmurs. The current iteration of the system boasts an impressive accuracy and sensitivity rate of 90%. This study is designed as a randomized controlled trial (RCT) to be conducted at Shanghai Xinhua Hospital and Qinghai Provincial Women and Children's Hospital. The primary objective is to demonstrate the superiority of AI-assisted primary care physicians in identifying CHD over primary care physicians working independently. This will be achieved by conducting a comparative analysis of the performance of AI-assisted physicians versus their unassisted counterparts, thereby substantiating the model's practical applicability. Through an ongoing process of refinement and widespread application, this pioneering research endeavors to empower a diverse range of medical professionals, including general practitioners, child health physicians, and non-cardiovascular specialists, with the transformative capabilities of AI-assisted electronic auscultation. The ultimate goal is to elevate the standard of pediatric care across the nation.

Conditions

Congenital Heart Disease (CHD); Screening Tool; Artifical Intelligence; Randomised Controlled Trial; Auscultation for Clinical Evaluation

Outcome Measures

Primary Outcomes (1)

• Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation

  From enrollment to the end of treatment at 3 months

Secondary Outcomes (6)

• Specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation

  From enrollment to the end of treatment at 3 months

• Specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & AI-Assisted Auscultation By Primary Care Physician

  From enrollment to the end of treatment at 3 months

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

  From enrollment to the end of treatment at 3 months

• Specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & Primary Care Physicians' Independent Auscultation

  From enrollment to the end of treatment at 3 months

• Sensitivity, specificity, accuracy, and false negatives of auscultation in CHD detection: Specialist Physicians' Independent Auscultation & AI Model

  From enrollment to the end of treatment at 3 months

Study Arms (2)

Independent auscultation

ACTIVE COMPARATOR

Diagnostic Test: Independent auscultation

AI-assisted auscultation

EXPERIMENTAL

Diagnostic Test: AI-assisted auscultation

Interventions

AI-assisted auscultation DIAGNOSTIC_TEST

The study begins with non-blinded staff collecting medical histories and specialist physicians conducting face-to-face auscultations and assessments. Then, primary care doctors will conduct face-to-face auscultations and first assessments, and use AI-assisted stethoscopes to collect heart sounds following a set protocol. The AI model will analyze the data in real-time and provides an immediate diagnostic result, which is relayed back to the primary care physicians. Based on this, they will make a secondary assessment. All participants will undergo echocardiography.

AI-assisted auscultation

Independent auscultation DIAGNOSTIC_TEST

It includes medical history collection by non-blinded independent personnel, face-to-face auscultation and evaluations conducted by specialist physicians and primary care doctors separately. All participants will undergo echocardiography.

Independent auscultation

Eligibility Criteria

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

You may qualify if:

• Age between 0 to 18 years, with no gender restrictions.
• Children who consent to undergo echocardiography to determine the presence or absence of congenital heart disease.
• Voluntary participation in this study and signing of an informed consent form.

You may not qualify if:

• Age greater than 18 years.
• Children who are unable to undergo echocardiography or who do not cooperate with auscultation.
• Participants who cannot provide informed consent or are unwilling to comply with study requirements to provide medical data for further analysis and research.

Sponsors & Collaborators

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

Study Sites (2)

Qinghai Provincial Women and Children's Hospital

Qinghai, China

NOT YET RECRUITING

Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine

Shanghai, 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: February 10, 2025
• Primary Completion: May 28, 2025
• Study Completion: June 30, 2025
• Last Updated: April 1, 2025

Record last verified: 2025-01

Locations

CN (2)