Research on Body Voice AI Recognition System for Children's Health Management

August 11, 2026 updated by: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Estimated)

30000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Hubei
      • Wuhan, Hubei, China, 430016
        • Recruiting
        • Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology
        • Contact:
    • Hunan
      • Changsha, Hunan, China, 410007
        • Recruiting
        • Human Children's Hospital
        • Contact:
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, China, 200092
        • Recruiting
        • Xinhua Hospital,Shanghai Jiao Tong University School of Medicine
        • Contact:
      • Shanghai, Shanghai Municipality, China, 200127
        • Recruiting
        • Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine
        • Contact:
    • Yunnan
      • Kunming, Yunnan, China, 650028
        • Recruiting
        • Kunming Children's hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  1. Age 0~18 years old, gender is not limited
  2. Children who have been diagnosed with congenital heart disease by cardiac ultrasound or who do not have congenital heart disease
  3. Children diagnosed with bronchopneumonia or without bronchopneumonia
  4. Children who are clinically diagnosed with intestinal diseases or who do not suffer from intestinal diseases
  5. Informed consent

Exclusion Criteria:

  1. ≥ 18 years old
  2. Children who are unable to undergo cardiac ultrasound, chest imaging or other related examinations
  3. 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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Diagnostic test: Heart Auscultation and Echocardiography
Heart auscultation will be done by pediatrician and echocardiography by echocardiologist
Diagnostic test: Chest Auscultation and Chest imaging examinations
Chest auscultation will be done by pediatrician and chest imaging examinations by radiologist
Diagnostic test: Abdominal Auscultation and Abdominal imaging examinations
Abdominal auscultation will be done by pediatrician and chest imaging examinations by radiologist

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity
Time Frame: 1 month
Sensitivity in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
1 month
Specificity
Time Frame: 1 month
Specificity in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
1 month
AUC
Time Frame: 1 month
AUC in CHD, lung disease and abdominal screening by different artificial intelligence algorithm and auscultation
1 month

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Xinhua Hospital, Shanghai Jiao Tong University School of Medicine

Investigators

  • Principal Investigator: Xin Sun, MD, Xinhua Hospital, Shanghai J iao Tong University School of Medicine

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 1, 2024

Primary Completion (Estimated)

December 31, 2026

Study Completion (Estimated)

December 31, 2026

Study Registration Dates

First Submitted

August 4, 2024

First Submitted That Met QC Criteria

August 6, 2024

First Posted (Actual)

August 7, 2024

Study Record Updates

Last Update Posted (Actual)

August 12, 2026

Last Update Submitted That Met QC Criteria

August 11, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • XH-24-005

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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