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.

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.

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.

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.