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

Shanghai Jiao Tong University

Status: Not yet enrolling

Conditions: Congenital Heart Disease; Abdominal Disease; Bronchopneumonia

Treatments: Diagnostic Test: Chest Auscultation and Chest imaging examinations; Diagnostic Test: Heart Auscultation and Echocardiography; Diagnostic Test: Abdominal Auscultation and Abdominal imaging examinations

Study type: Observational

Funder types: Other

Identifiers

NCT06542120

XH-24-005

Details and patient eligibility

About

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.

Full 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.

Enrollment: 30,000 estimated patients

Sex: All

Ages: Under 18 years old

Volunteers: Accepts Healthy Volunteers

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.

Trial design

30,000 participants in 1 patient group

0 ~ 18 years old children

Description:

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.

Treatment:

Diagnostic Test: Abdominal Auscultation and Abdominal imaging examinations

Diagnostic Test: Heart Auscultation and Echocardiography

Diagnostic Test: Chest Auscultation and Chest imaging examinations