Congenital Heart DiseaseNCT06542120
Research on Body Voice AI Recognition System for Children's Health Management
An observational study of Congenital Heart Disease and Bronchopneumonia, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 5 sites in China.
Trial summary
The purpose of this research is to develop a body voice artificial intelligence (AI) recognition device, also referred to as an AI-assisted body sound identification device, by utilizing a deep learning-based novel AI algorithm in conjunction with a big body voice model. 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, Bronchopneumonia, Abdominal Disease
- What is being tested?
- Heart Auscultation and Echocardiography, Chest Auscultation and Chest imaging examinations and Abdominal Auscultation and Abdominal imaging examinations
- How many people
- 30,000 participants
- Where
- 5 sitesChina
- Study dates
- 1 May 2024to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: 1. Children (0-18 years old) with congenital heart disease confirmed by cardiac ultrasound and children without congenital heart disease confirmed by cardiac ultrasound. 2. Children (0-18 years old) with bronchopneumonia confirmed by chest imaging examination and children without bronchopneumonia confirmed by imaging. 3. Children (0-18 years old) with abdominal imaging confirmed abdominal disease and children diagnosed without abdominal disease by imaging.
Who may be able to join
- Age 0~18 years old, gender is not limited
- Children who have been diagnosed with congenital heart disease by cardiac ultrasound or who do not have congenital heart disease
- Children diagnosed with bronchopneumonia or without bronchopneumonia
- Children who are clinically diagnosed with intestinal diseases or who do not suffer from intestinal diseases
- Informed consent
Who cannot join
- ≥ 18 years old
- Children who are unable to undergo cardiac ultrasound, chest imaging or other related examinations
- Subjects who are unable to obtain informed consent, or who are unwilling to cooperate with the provision of diagnosis and treatment related data for further analysis and research as required by the study.
Trial locations and contacts
5 sites in 1 country. Contact the study team or a site near you to ask about taking part.
5 sites in 1 country
China5 sites, 5 recruiting
- Recruiting
Human Children's Hospital
Changsha, Hunan, 410007
Zhi Chen, MD, 13787028209, eychenzhi@163.com
- Recruiting
Kunming children's Hospital
Kunming, Yunnan, 650028
Jieqing Min, 13888867101, 24147863@qq.com
- Recruiting
Xinhua Hospital,Shanghai Jiao Tong University School of Medicine
Shanghai, Shanghai Municipality, 200092
Xin Sun, MD, 0086-21-25077480, sunxin@xinhuamed.com.cn
- Recruiting
Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine
Shanghai, Shanghai Municipality, 200127
Yiwei Chen, MD, 13564543777, chenyiwei@scmc.com.cn
- Recruiting
Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430016
Yong Zhang, 13886842352, 1539210298@qq.com
About this study
Official title: Intelligent Voice Model: A New Paradigm Exploration for Child Health Management
The purpose of this research is to develop a body voice artificial intelligence (AI) recognition device, also referred to as an AI-assisted body sound identification device, by utilizing a deep learning-based novel AI algorithm in conjunction with a big body voice model. It could identify normal and abnormal heart, breath, and bowel sounds, and to provide early screening and auxiliary diagnosis of congenital heart disease (CHD), respiratory infections, diarrhea and other common multi-occurring diseases.
Read the detailed description
The study employed a multicenter cross-sectional design. The real-world data collected for this study included normal and definitively diagnosed heart sounds in children with congenital heart disease, normal and definitively diagnosed respiratory tract infections in children with breath sounds, specific cough sounds, and normal and definitively diagnosed children's bowel sounds with diarrhea. The specialist team will carry out data governance, annotation, and feature sound extraction on the gathered normal and aberrant sounds, in order to generate a superior multimodal training dataset. Large model artificial intelligence algorithms (deep learning, machine learning, etc.) are used to model and train the algorithm model of the body voice AI recognition device, so that it can distinguish between normal and abnormal sound signals by AI. The results of body sound AI identification will be compared with diagnostic reports from echocardiograms, chest X-rays, and belly X-rays in terms of AUC (Area Under Curve) score, sensitivity, specificity, and accuracy to evaluate the impact of AI recognition devices on illness screening and supplementary diagnosis. External validation will be conducted using homogeneous data from other sites. This project aims to develop a new generation of intelligent sound auscultation instruments that could be used for early screening and auxiliary diagnosis of congenital heart disease , respiratory infections, diarrhea and other common multi-occurring diseases by utilizing large model artificial intelligence technologies.
- Being tested
- Heart Auscultation and Echocardiography, Chest Auscultation and Chest imaging examinations, Abdominal Auscultation and Abdominal imaging examinations
- Medical subject headings
- Heart Defects, Congenital, Bronchopneumonia
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 pediatrician and echocardiography by echocardiologist
Chest Auscultation and Chest imaging examinationsDiagnostic test
Chest auscultation will be done by pediatrician and chest imaging examinations by radiologist
Abdominal Auscultation and Abdominal imaging examinationsDiagnostic test
Abdominal auscultation will be done by pediatrician and chest imaging examinations by radiologist
| Study group | Role | What they receive |
|---|---|---|
| 0 ~ 18 years old children | Age range: 0 to 18 years old, with no gender restriction. Children who have been diagnosed with congenital heart disease (CHD) or confirmed to be free of CHD through echocardiographic examinations.
Children who have been diagnosed with bronchopneumonia or confirmed to be free of bronchopneumonia through chest imaging examinations.
Children who have been diagnosed with abdominal diseases or confirmed to be free of abdominal diseases through abdominal imaging examinations. Receives: Diagnostic Test: Heart Auscultation and Echocardiography; Diagnostic Test: Chest Auscultation and Chest imaging examinations; Diagnostic Test: Abdominal Auscultation and Abdominal imaging examinations |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity
Sensitivity in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
Measured: 1 month
Specificity
Specificity in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
Measured: 1 month
AUC
AUC in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Aug 2024
- Study start1 May 2024
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Congenital Heart DiseaseStudies 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
- 30,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xin Sun, MD, Principal investigator, Xinhua Hospital, Shanghai J iao Tong University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06542120
- Sponsor's protocol ID
- XH-24-005
- Status last verified
- Apr 2026
- First submitted
- 4 Aug 2024
- Last updated
- 12 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Intelligent Voice Model: A New Paradigm Exploration for Child Health Management
- ClinicalTrials.gov ID
- NCT06542120
- Acronym / sponsor ID
- XH-24-005
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 30,000 participants (estimated)
- Lead sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of MedicineUniversity, hospital or other
- Study start
- 1 May 2024 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 7 Aug 2024 (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 purpose of this research is to develop a body voice artificial intelligence (AI) recognition device, also referred to as an AI-assisted body sound identification device, by utilizing a deep learning-based novel AI algorithm in conjunction with a big body voice model. It could identify normal and abnormal heart, breath, and bowel sounds, and to provide early screening and auxiliary diagnosis of congenital heart disease (CHD), respiratory infections, diarrhea and other common multi-occurring diseases.
Detailed description
The study employed a multicenter cross-sectional design. The real-world data collected for this study included normal and definitively diagnosed heart sounds in children with congenital heart disease, normal and definitively diagnosed respiratory tract infections in children with breath sounds, specific cough sounds, and normal and definitively diagnosed children's bowel sounds with diarrhea. The specialist team will carry out data governance, annotation, and feature sound extraction on the gathered normal and aberrant sounds, in order to generate a superior multimodal training dataset. Large model artificial intelligence algorithms (deep learning, machine learning, etc.) are used to model and train the algorithm model of the body voice AI recognition device, so that it can distinguish between normal and abnormal sound signals by AI. The results of body sound AI identification will be compared with diagnostic reports from echocardiograms, chest X-rays, and belly X-rays in terms of AUC (Area Under Curve) score, sensitivity, specificity, and accuracy to evaluate the impact of AI recognition devices on illness screening and supplementary diagnosis. External validation will be conducted using homogeneous data from other sites. This project aims to develop a new generation of intelligent sound auscultation instruments that could be used for early screening and auxiliary diagnosis of congenital heart disease , respiratory infections, diarrhea and other common multi-occurring diseases by utilizing large model artificial intelligence technologies.
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 Echocardiography, Diagnostic Test: Chest Auscultation and Chest imaging examinations, Diagnostic Test: Abdominal Auscultation and Abdominal imaging examinationsAge range: 0 to 18 years old, with no gender restriction. Children who have been diagnosed with congenital heart disease (CHD) or confirmed to be free of CHD through echocardiographic examinations. Children who have been diagnosed with bronchopneumonia or confirmed to be free of bronchopneumonia through chest imaging examinations. Children who have been diagnosed with abdominal diseases or confirmed to be free of abdominal diseases through abdominal imaging examinations. |
Who may qualify
- Ages
- Up to 18 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- 1. Children (0-18 years old) with congenital heart disease confirmed by cardiac ultrasound and children without congenital heart disease confirmed by cardiac ultrasound. 2. Children (0-18 years old) with bronchopneumonia confirmed by chest imaging examination and children without bronchopneumonia confirmed by imaging. 3. Children (0-18 years old) with abdominal imaging confirmed abdominal disease and children diagnosed without abdominal disease by imaging.
Key inclusion criteria (5 listed)
- Age 0~18 years old, gender is not limited
- Children who have been diagnosed with congenital heart disease by cardiac ultrasound or who do not have congenital heart disease
- Children diagnosed with bronchopneumonia or without bronchopneumonia
- Children who are clinically diagnosed with intestinal diseases or who do not suffer from intestinal diseases
- Informed consent
Key exclusion criteria (3 listed)
- ≥ 18 years old
- Children who are unable to undergo cardiac ultrasound, chest imaging or other related examinations
- Subjects who are unable to obtain informed consent, or who are unwilling to cooperate with the provision of diagnosis and treatment related data for further analysis and research as required by the study.
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 pediatrician and echocardiography by echocardiologist | Diagnostic test | 0 ~ 18 years old children |
| Chest Auscultation and Chest imaging examinationsChest auscultation will be done by pediatrician and chest imaging examinations by radiologist | Diagnostic test | 0 ~ 18 years old children |
| Abdominal Auscultation and Abdominal imaging examinationsAbdominal auscultation will be done by pediatrician and chest imaging examinations by radiologist | Diagnostic test | 0 ~ 18 years old children |
Primary outcome
Sensitivity
Time frame: 1 month
Sensitivity in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
Specificity
Time frame: 1 month
Specificity in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
AUC
Time frame: 1 month
AUC in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Aug 2026.
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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.
Xin Sun, MD principal investigator
Xinhua Hospital, Shanghai J iao Tong University School of Medicine
5 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 and Bronchopneumonia, run by Xinhua Hospital, Shanghai Jiao Tong University School of Medicine at 5 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 5 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: looking for participants now, as of the sponsor's last update on 12 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 5 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Xin Sun, MD (0086-021-25077480, sunxin@xinhuamed.com.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity (time frame: 1 month). The study lists 3 primary outcomes in all. 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 12 Aug 2026.