Multimodal Assisted Diagnosis for Pediatric Respiratory Diseases Using Questionnaires, Cough Sounds, and Breath Sounds

A Prospective Observational Study of Multimodal Assisted Diagnosis for Pediatric Respiratory Diseases Using Symptom Questionnaires, Cough Sounds, and Breath Sounds

This study aims to establish a standardized, synchronized data collection system for pediatric symptom questionnaires, cough sounds, and breath sounds, and to construct a multimodal database of pediatric respiratory diseases including both disease cases and healthy controls. Using the final research labels determined by clinicians' diagnoses, health status assessments, and research team review as the reference standard, this study will develop and validate a multimodal assisted diagnostic model for common pediatric respiratory diseases based on symptom questionnaires, cough sounds, and breath sounds. The study will primarily evaluate the diagnostic performance of the model in distinguishing healthy children from children with respiratory diseases, screening for asthma and asthma-related cough, and identifying pneumonia, tracheitis/bronchitis, upper airway-related diseases, and common causes of chronic cough. It will also assess the incremental value of cough sounds and breath sounds beyond symptom questionnaire information.

This is a prospective, observational diagnostic accuracy study to be conducted at Shanghai Children's Medical Center. The study population will include children presenting with cough, wheezing, fever with respiratory symptoms, nasal congestion, rhinorrhea, sore throat, or other respiratory complaints, as well as healthy children recruited during routine health examinations. The study will establish a standardized and synchronized data collection workflow for pediatric symptom questionnaires, cough sounds, and breath sounds. All enrolled participants will complete a structured symptom questionnaire, undergo cough sound recording using a smartphone application, and undergo breath sound recording using an electronic stethoscope under unified protocols. Demographic and clinical information, including age, sex, disease duration, major symptoms, medical history, allergy history, family history, medication use, final clinical diagnosis, or health status assessment, will also be collected to construct a multimodal database of pediatric respiratory diseases including both disease cases and healthy controls. Based on this database, the study will develop a stepwise multimodal assisted diagnostic framework using a combination of conventional statistical learning and deep learning methods. Three diagnostic models will be constructed and compared: a symptom questionnaire-only model, a symptom questionnaire plus cough sound model, and a multimodal model integrating symptom questionnaires, cough sounds, and breath sounds. Using the final research labels determined by clinicians' diagnoses, health status assessments, and research team review as the reference standard, the study will evaluate the diagnostic performance of these models in distinguishing healthy children from children with respiratory diseases, screening for asthma and asthma-related cough, and identifying pneumonia, tracheitis/bronchitis, upper airway-related diseases, and common causes of chronic cough. Model performance will be assessed using AUC, AUPRC, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and accuracy. The study will further investigate the incremental value of cough sounds and breath sounds beyond symptom questionnaire information, and assess model stability and generalizability across different age groups, clinical settings, and device conditions. The findings are expected to provide evidence for the optimization, clinical translation, and potential home-based extension of multimodal artificial intelligence-assisted diagnostic models for pediatric respiratory diseases. The study will not interfere with routine clinical care, and the model outputs will not be used for real-time clinical decision-making.

Inclusion Criteria:

Children aged 28 days to 18 years, regardless of sex, will be eligible for inclusion. The disease group will include children presenting to the outpatient department, emergency department, or inpatient wards of Shanghai Children's Medical Center with cough, wheezing, fever with respiratory symptoms, nasal congestion, rhinorrhea, sore throat, or other respiratory complaints. Participants should be able to complete the symptom questionnaire, cough sound recording, and breath sound recording, and their guardians must provide informed consent and allow review of relevant medical history and diagnostic information. The healthy control group will include children recruited from routine health examinations or children without respiratory complaints, with no acute respiratory symptoms within the past 4 weeks, no known history of chronic respiratory diseases, no obvious respiratory abnormalities on health assessment or research team review, the ability to complete relevant data collection, and guardian informed consent.

Exclusion Criteria:

Participants will be excluded if they have severe cardiopulmonary malformations, long-term tracheostomy or mechanical ventilation, severe neuromuscular disorders, severe immunodeficiency, or other conditions that may substantially alter cough sound characteristics or affect the clinical presentation of respiratory diseases. Children whose primary diagnosis at the current visit is a non-respiratory disease and who are not suitable for this study will also be excluded. Participants who are unable to complete the symptom questionnaire, or whose cough sound or breath sound recordings remain of insufficient quality after repeated attempts, will not be included or will be excluded from the corresponding modality-specific analysis. Participants with only one unavailable modality may be included in analyses based on the completed modalities but will be excluded from analyses requiring the missing modality. Healthy controls will be excluded from the main control analysis if recent respiratory symptoms, a history of chronic respiratory disease, or other conditions that may affect acoustic features are identified.