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

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

Interventions

DIAGNOSTIC_TEST

AI-assisted auscultation

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.

DIAGNOSTIC_TEST

Independent auscultation

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.

Sponsors & Collaborators

• Bill and Melinda Gates Foundation

  collaborator OTHER

• Kun Sun

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

NONE

Model

PARALLEL

Eligibility

Max Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-02-10

Primary Completion

2025-02-27

Completion

2025-12-31

Countries

• China

Study Locations