Clinical trial NCT06542120: Research on Body Voice AI Recognition System for Children's Health Management
Official title: Intelligent Voice Model: A New Paradigm Exploration for Child Health Management
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Congenital Heart Disease
Bronchopneumonia
Abdominal Disease
Lead sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Sponsor class: OTHER
First posted: 2024-08-07
Enrollment: 30000
Sex: ALL
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
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.

Brief 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. 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Sensitivity
timeFrame: 1 month
measure: Specificity
timeFrame: 1 month
measure: AUC
timeFrame: 1 month
Secondary outcomes:
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
city: Changsha
country: China
facility: Human Children's Hospital
state: Hunan
city: Shanghai
country: China
facility: Xinhua Hospital,Shanghai Jiao Tong University School of Medicine
state: Shanghai
city: Shanghai
country: China
facility: Shanghai Children's Medical Center Affiliated to Shanghai Jiaotong University School of Medicine
state: Shanghai
city: Kunming
country: China
facility: Kunming children's Hospital
state: Yunnan
Study officials:
affiliation: Xinhua Hospital, Shanghai J iao Tong University School of Medicine
name: Xin Sun, MD
role: PRINCIPAL_INVESTIGATOR
References: