AI-Assisted Smart Stethoscope Screening for Structural Heart Disease in School Students in Ruyang County

NCT ID: NCT07194785

Last Updated: 2025-09-26

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 10000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-10-20
• Study Completion Date: 2026-03-31

Brief Summary

The goal of this observational diagnostic study is to evaluate whether an artificial intelligence (AI)-enabled smart stethoscope can accurately detect structural heart disease in school-aged children and adolescents (10-18 years) in Ruyang County, China.

The main questions it aims to answer are:

Can the smart stethoscope reliably identify students with cardiac murmurs that indicate possible structural heart disease? How well do the sensitivity, specificity, and predictive values of the smart stethoscope compare with standard echocardiography?

Researchers will compare AI-assisted stethoscope screening results with echocardiography (gold standard) to see if the device can be used as an effective early screening tool.

Participants will:

Undergo a heart sound screening using the AI-enabled smart stethoscope (3-5 minutes).

If screening is positive, receive a free echocardiogram at Ruyang County People's Hospital.

A small sample of students with negative screening results will also receive echocardiography to check for missed cases.

Detailed Description

Structural heart disease (SHD), including congenital and acquired cardiac abnormalities, is a leading cause of morbidity in children and adolescents. Cardiac murmurs are common clinical signs, but traditional auscultation has limited accuracy in school or community settings due to examiner variability and limited access to echocardiography.

This study evaluates the performance of an artificial intelligence (AI)-enabled smart stethoscope for school-based screening of SHD in primary and secondary students in Ruyang County, China. The device integrates high-sensitivity acoustic sensors, noise-reduction technology, and deep learning algorithms to provide automated interpretations of heart sounds within seconds. Prior validation studies have demonstrated high sensitivity (>80%) and specificity (>90%) for congenital heart disease and up to 94% sensitivity and 98% specificity for rheumatic heart disease.

Screening will be conducted by trained personnel at four standard cardiac auscultation sites. Students with abnormal AI findings will undergo repeat testing and, if confirmed, will be referred for transthoracic echocardiography at Ruyang County People's Hospital. A subset of students with negative AI screens will also receive echocardiography to estimate false-negative rates.

Data will be analyzed using 2×2 contingency tables to compare AI screening results with echocardiography, and diagnostic performance metrics including sensitivity, specificity, positive predictive value, and negative predictive value will be calculated with 95% confidence intervals. Agreement between AI-assisted auscultation and echocardiography will be assessed using Cohen's kappa.

This study will provide evidence on the feasibility, accuracy, and scalability of AI-enabled smart stethoscopes for early SHD detection in school-based, low-resource settings.

Conditions

Structural Heart Disease

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Screen group

AI-Assisted Cardiac Auscultation using the HearTech Smart Stethoscope

Intervention Type: DIAGNOSTIC_TEST

This intervention utilizes the HearTech smart stethoscope, where trained research personnel perform standardized examinations of four cardiac auscultation areas on subjects. The integrated AI algorithm analyzes heart sounds in real time and automatically generates reports. An initial positive detection triggers a repeat testing process, with the algorithm ultimately determining a positive screening result based on three detection outcomes (any two positive). This AI-assisted auscultation system is designed to achieve large-scale, standardized, and highly efficient preliminary heart murmur screening.

Interventions

AI-Assisted Cardiac Auscultation using the HearTech Smart Stethoscope

This intervention utilizes the HearTech smart stethoscope, where trained research personnel perform standardized examinations of four cardiac auscultation areas on subjects. The integrated AI algorithm analyzes heart sounds in real time and automatically generates reports. An initial positive detection triggers a repeat testing process, with the algorithm ultimately determining a positive screening result based on three detection outcomes (any two positive). This AI-assisted auscultation system is designed to achieve large-scale, standardized, and highly efficient preliminary heart murmur screening.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. School students aged 10-18 years.

2. Able to cooperate with cardiac auscultation.

3. Student and parent/guardian provide written informed consent.

Exclusion Criteria

1. Student or parent/guardian declines participation.

2. Inability or unwillingness to follow screening procedures or cooperate with cardiac auscultation.

3. Refusal to undergo standard transthoracic echocardiography or cardiology evaluation.

4. Previously diagnosed structural heart disease.

5. Chest wall deformities or skin conditions that may interfere with auscultation.

6. Fever ≥37.5 °C on the day of examination, or severe developmental delay or other conditions preventing cooperation with the examination.

• Minimum Eligible Age: 10 Years
• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Heart Health Research Center

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Ruyang County People's Hospital

Luoyang, Henan, China

Countries

China

Central Contacts

Yanna Song

Role: CONTACT

songyanna@RRH.onaliyun.com

+86 17839372777

Rong Han

Role: CONTACT

hanrong@hhresearch.cn

+86 13910669903

Other Identifiers

RY-Screen

Identifier Type: -

Identifier Source: org_study_id