Development of an AI Assessment System for Pediatric Respiratory Distress : A Prospective Study

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.