Congenital Heart Disease in ChildrenNCT04307030
Study on AI Recognition System Of Heart Sound In Congenital Heart Disease Screening
An observational study of Congenital Heart Disease in Children, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 16 sites in China.
Trial summary
The objective of this study is to establish AI algorithm based on the deep learning to strengthen the ability to classify the heart murmurs of healthy people and different major or other subdivided congenital heart diseases(CHDs) and to evaluate the effectiveness of artificial intelligence technology-assisted heart… 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 in Children
- What is being tested?
- Heart Auscultation and Echocardiography
- How many people
- 9,370 participants
- Where
- 16 sitesChina
- Study dates
- 1 Jul 2020to 1 May 2024
- Phase
- Observational
- 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
Study population: Pediatric population during outpatient service or hospitalization in ten pediatric medical centers in CHINA
Who may be able to join
- 0 ~ 18 years of age, regardless of gender ；
- Children with or without congenital heart disease confirmed by echocardiography；
- On the basis of informed consent，willing to cooperate with our group.
Who cannot join
- ≥ 18 years of age;
- Children who can not undergo echocardiography or other related tests;
- Subjects who refuse to join in, or who are unwilling to cooperate with the provision of diagnostic and therapeutic data for further analysis and research.
Trial locations and contacts
16 sites in 1 country.
16 sites in 1 country
China16 sites
Beijing Anzhen Hospital
Beijing
Children's Hospital Affiliated to Chongqing Medical University
Chongqing
The First Affiliated Hospital of Guangxi Medical University
Guangxi
Guangzhou Women and Children's Medical Center
Guangzhou
Children's Hospital Affiliated to Zhejiang Medical University
Hangzhou
Hunan Children's Hospital
Hunan
Shandong Provincial Hospital
Jinan
Kunming Children's Hospital
Kunming
Lanzhou University Second Hospital
Lanzhou
Linyi Hospital for Women and Children
Linyi
Children's Hospital of Shanghai
Shanghai
Shanghai Children's Medical Center
Shanghai
Shiyan Taihe Hospital
Shiyan
Children's Hospital of Soochow University
Suzhou
The Second Hospital Affiliated to Wenzhou Medical University
Wenzhou
Wuhan Children's Hospital
Wuhan
About this study
Official title: Multi-center Study on Exploration and Application of Artificial Intelligence Technology-Assisted Heart Sound Recognition System in Children's Congenital Heart Disease Screening
The objective of this study is to establish AI algorithm based on the deep learning to strengthen the ability to classify the heart murmurs of healthy people and different major or other subdivided congenital heart diseases(CHDs) and to evaluate the effectiveness of artificial intelligence technology-assisted heart sound recognition system (referred to as: Heart sound AI recognition system) for multi-center CHD screening.
Read the detailed description
This is a multi-center cluster cross-sectional study in CHINA. Heart sounds will be collected by auscultation using an electronic stethoscope in children (0 ~ 18 years old) confirmed with or without CHDs by echocardiography during outpatient or hospitalization in 10 pediatric medical centers. Heart sounds will be visualized as phonocardiogram, and feature extraction will be done after classification of normal and abnormal heart sounds and labeling the characteristics of heart murmurs by pediatric cardiovascular specialists. Artificial intelligence algorithm (machine learning, deep learning, etc.) will be trained to build a heart sounds recognition system with the data mentioned above.We will use the receiver operating characteristic (ROC) curve to compare the ability of recognition and classification of abnormal heart sounds between different artificial intelligence algorithm. Taken the results of echocardiography as the gold standard, we will use the evaluation indexes,such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity of CHD screening between the AI recognition system and human cardiovascular pediatricians. Our target is to use artificial intelligence technology to assist heart auscultation for CHD screening.
- Conditions
- Congenital Heart Disease in Children
- Being tested
- Heart Auscultation and Echocardiography
- Keywords
- Congenital Heart Disease, Artificial Intelligence, Heart Sound
- 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.
Heart Auscultation and EchocardiographyDiagnostic test
Heart auscultation will be done by cardiovascular pediatrician and echocardiography by echocardiologist
| Study group | Role | What they receive |
|---|---|---|
| 0 ~ 18 years old children | Children During Outpatient or Hospitalization Receives: Diagnostic Test: Heart Auscultation and Echocardiography |
What is being measured
The primary outcome is the main question the study is designed to answer.
Receiver operating characteristic (ROC) of sensitivity
ROC of sensitivity in CHD screening by different artificial intelligence algorithm and auscultation
Measured: July 2020 to December 2021
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Mar 2020
- Study start1 Jul 2020
- Primary completion1 May 2024
- Study completion1 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Congenital Heart Disease in ChildrenStudies of the same condition, open and under way.
- More trials run by Xinhua Hospital, Shanghai Jiao Tong University School of MedicineThe sponsor's other registered studies.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 9,370 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kun Sun, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Investigators
- KUN SUN, MD, Principal investigator, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04307030
- Sponsor's protocol ID
- XH-20-003
- Status last verified
- May 2024
- First submitted
- 11 Mar 2020
- Last updated
- 3 Aug 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 3 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multi-center Study on Exploration and Application of Artificial Intelligence Technology-Assisted Heart Sound Recognition System in Children's Congenital Heart Disease Screening
- ClinicalTrials.gov ID
- NCT04307030
- Acronym / sponsor ID
- XH-20-003
- Conditions
- Congenital Heart Disease in Children
- Keywords
- Congenital Heart Disease, Artificial Intelligence, Heart Sound
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 9,370 participants (actual)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 1 Jul 2020 (actual)
- Primary completion
- 1 May 2024 (actual)
- Study completion
- 1 May 2024 (actual)
- First posted
- 13 Mar 2020 (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.
The objective of this study is to establish AI algorithm based on the deep learning to strengthen the ability to classify the heart murmurs of healthy people and different major or other subdivided congenital heart diseases(CHDs) and to evaluate the effectiveness of artificial intelligence technology-assisted heart sound recognition system (referred to as: Heart sound AI recognition system) for multi-center CHD screening.
Detailed description
This is a multi-center cluster cross-sectional study in CHINA. Heart sounds will be collected by auscultation using an electronic stethoscope in children (0 ~ 18 years old) confirmed with or without CHDs by echocardiography during outpatient or hospitalization in 10 pediatric medical centers. Heart sounds will be visualized as phonocardiogram, and feature extraction will be done after classification of normal and abnormal heart sounds and labeling the characteristics of heart murmurs by pediatric cardiovascular specialists. Artificial intelligence algorithm (machine learning, deep learning, etc.) will be trained to build a heart sounds recognition system with the data mentioned above.We will use the receiver operating characteristic (ROC) curve to compare the ability of recognition and classification of abnormal heart sounds between different artificial intelligence algorithm. Taken the results of echocardiography as the gold standard, we will use the evaluation indexes,such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity of CHD screening between the AI recognition system and human cardiovascular pediatricians. Our target is to use artificial intelligence technology to assist heart auscultation for CHD screening.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 0 ~ 18 years old children | – | Diagnostic Test: Heart Auscultation and EchocardiographyChildren During Outpatient or Hospitalization |
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- Pediatric population during outpatient service or hospitalization in ten pediatric medical centers in CHINA
Key inclusion criteria (3 listed)
- 0 ~ 18 years of age, regardless of gender ；
- Children with or without congenital heart disease confirmed by echocardiography；
- On the basis of informed consent，willing to cooperate with our group.
Key exclusion criteria (3 listed)
- ≥ 18 years of age;
- Children who can not undergo echocardiography or other related tests;
- Subjects who refuse to join in, or who are unwilling to cooperate with the provision of diagnostic and therapeutic 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 |
|---|---|---|
| Heart Auscultation and EchocardiographyHeart auscultation will be done by cardiovascular pediatrician and echocardiography by echocardiologist | Diagnostic test | 0 ~ 18 years old children |
Primary outcome
Receiver operating characteristic (ROC) of sensitivity
Time frame: July 2020 to December 2021
ROC of sensitivity in CHD screening by different artificial intelligence algorithm and auscultation
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 3 Aug 2026.
- Individual participant dataSharing planned: Undecided
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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.
KUN SUN, MD principal investigator
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
16 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Congenital Heart Disease in Children, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 16 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 3 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 16 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?
Receiver operating characteristic (ROC) of sensitivity (time frame: July 2020 to December 2021). 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 3 Aug 2026.