Congenital Heart Disease (CHD)NCT06791096
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
A study testing AI-assisted auscultation and Independent auscultation in people with Congenital Heart Disease (CHD) and Screening Tool, run by Kun Sun at 2 sites in China.
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. Read more about this study
- Who is it for?
- Ages Up to 18 yearsHealthy volunteers can take part
- Conditions the study is for
- Congenital Heart Disease (CHD), Screening Tool, Artifical Intelligence
- What is being tested?
- AI-assisted auscultation and Independent auscultation
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 212 participants
- Where
- 2 sitesChina
- Study dates
- 10 Feb 2025to 31 Dec 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Up to 18 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country.
China2 sites
Qinghai Provincial Women and Children's Hospital
Qinghai
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine
Shanghai
About this study
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
- Being tested
- AI-assisted auscultation, Independent auscultation
- Medical subject headings
- Heart Defects, Congenital
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted auscultationDiagnostic 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.
Independent auscultationDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Independent auscultation | Active comparator | Not described Receives: Diagnostic Test: Independent auscultation |
| AI-assisted auscultation | Experimental | Not described Receives: Diagnostic Test: AI-assisted auscultation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: 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
Measured: From enrollment to the end of treatment at 3 months
The rate of diagnostic revisions by physicians, the proportions of correct and incorrect changes
Measured: From enrollment to the end of treatment at 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start10 Feb 2025
- Primary completion27 Feb 2025
- Study completion31 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Congenital Heart Disease (CHD)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 212 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kun Sun
- Sponsor type
- University, hospital or other
- Collaborators
- Bill and Melinda Gates Foundation
- Responsible party
- Sponsor-investigator, Kun Sun, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791096
- Sponsor's protocol ID
- XHEC-C-2025-012-1
- Other IDs
- INV-072724
- Status last verified
- Feb 2026
- First submitted
- 18 Jan 2025
- Last updated
- 7 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: IPD may not be shared or may be unavailable for sharing due to several reasons. Firstly, confidentiality is of utmost importance. IPD contains sensitive personal information, and sharing such data could potentially compromise the privacy of participants, thereby restricting the sharing of such detailed information. Secondly, the data is protected by intellectual property rights. This may limit its sharing without proper agreements or permissions. Thirdly, IPD is large in volume and complex in nature, requiring substantial resources for storage, transmission, and analysis. Detailed discussions and plans need to be formulated to ensure the integrity and security of the data during the sharing process.
Study at a glance
From the registry record, last updated by the sponsor on 7 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT06791096
- Acronym / sponsor ID
- XHEC-C-2025-012-1
- Conditions
- Congenital Heart Disease (CHD), Screening Tool, Artifical Intelligence, Randomised Controlled Trial, Auscultation for Clinical Evaluation
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 212 participants (actual)
- Lead sponsor
- Kun SunUniversity, hospital or other
- Collaborators
- Bill and Melinda Gates Foundation
- Study start
- 10 Feb 2025 (actual)
- Primary completion
- 27 Feb 2025 (actual)
- Study completion
- 31 Dec 2025 (actual)
- First posted
- 24 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Independent auscultation | Active comparator | Diagnostic Test: Independent auscultation |
| AI-assisted auscultation | Experimental | Diagnostic Test: AI-assisted auscultation |
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted auscultationThe 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 | AI-assisted auscultation |
| Independent auscultationIt 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. | Diagnostic test | Independent auscultation |
Primary outcome
Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
Time frame: From enrollment to the end of treatment at 3 months
6 secondary outcomes
Specificity, accuracy, and false negatives of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: 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
Time frame: From enrollment to the end of treatment at 3 months
The rate of diagnostic revisions by physicians, the proportions of correct and incorrect changes
Time frame: From enrollment to the end of treatment at 3 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Apr 2026.
- Individual participant dataSharing planned: Undecided. IPD may not be shared or may be unavailable for sharing due to several reasons. Firstly, confidentiality is of utmost importance. IPD contains sensitive personal information, and sharing such data could potentially compromise the privacy of participants, thereby restricting the sharing of such detailed information. Secondly, the data is protected by intellectual property rights. This may limit its sharing without proper agreements or permissions. Thirdly, IPD is large in volume and complex in nature, requiring substantial resources for storage, transmission, and analysis. Detailed discussions and plans need to be formulated to ensure the integrity and security of the data during the sharing process.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted auscultation and Independent auscultation in people with Congenital Heart Disease (CHD) and Screening Tool, run by Kun Sun at 2 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 7 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of auscultation in CHD detection: Primary Care Physicians' Independent Auscultation & AI-Assisted Auscultation (time frame: From enrollment to the end of treatment at 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Apr 2026.