This is a multicenter, prospective observational study designed to collect clinical data for the development of a vision-language model-based artificial intelligence system for automated assessment of pediatric respiratory patterns.

The study enrolls pediatric patients aged 0 to 12 years who present to the pediatric emergency departments of participating institutions. Clinical and visual respiratory data are collected along with baseline clinical characteristics, including sex, age, body weight, height, presenting symptoms recorded at emergency department arrival, initial vital signs (body temperature, pulse rate, respiratory rate, blood pressure, and oxygen saturation), severity at presentation assessed by the Korean Triage and Acuity Scale (KTAS), emergency department management and outcomes such as hospital admission or discharge, and other relevant clinical information.

These data are used for cohort characterization and for the development and evaluation of an AI-based system that aims to automatically analyze pediatric respiratory patterns and support objective respiratory assessment in pediatric emergency care.

Inclusion Criteria:

• Pediatric patients aged 0 to 12 years who present to participating pediatric emergency departments.
• Patients for whom clinical data, including basic demographic characteristics (e.g., age, body weight), severity at presentation (e.g., KTAS level), vital signs, emergency department management and outcomes, as well as visual respiratory data, are available during emergency care.

Exclusion Criteria:

• Patients outside the specified age range.
• Patients with insufficient or poor-quality clinical or visual respiratory data.
• Patients whose data cannot be used due to withdrawal of consent or regulatory restrictions.

The study population consists of pediatric patients aged 0 to 12 years who visit the pediatric emergency departments of participating institutions. Clinical data, including basic demographic characteristics (e.g., sex, age, body weight, height), severity at presentation (e.g., KTAS level), vital signs, and emergency department management and outcomes (e.g., admission or discharge), as well as visual respiratory data, are collected during emergency care and used for the development and evaluation of a vision-language model-based system for automated assessment of pediatric respiratory patterns.