Research Progress on New Non-invasive Diagnostic Techniques and Long-term Refined Management Strategies for Childhood Asthma

Abstract

Childhood bronchial asthma has clinical characteristics of strong heterogeneity, prolonged course, and easy recurrence of acute attacks. The atypical symptoms of preschool children and low cooperation with traditional testing have led to prominent problems such as early missed diagnosis, misdiagnosis, and poor long-term management. Traditional invasive and highly cooperative diagnostic methods are difficult to adapt to the physiological characteristics of children. The new non-invasive diagnostic technology, with its advantages of safety, convenience, and repeatable testing, gradually achieves early screening, inflammation classification, and risk prediction of asthma. At the same time, the traditional extensive management model cannot adapt to the individualized diagnosis and treatment needs of children with different phenotypes. Fine, full-cycle, and intelligent management has become the core direction of childhood asthma prevention and control. This article systematically reviews the clinical application value of new non-invasive diagnostic technologies for childhood asthma, covering cutting-edge technologies such as airway inflammation detection, respiratory acoustic monitoring, and intelligent assisted diagnosis. This article summarizes the research progress on refined long-term management strategies such as hierarchical classification, remote home monitoring, and physical and mental collaboration, providing an evidence-based reference for standardized and individualized comprehensive management of pediatric asthma.