# AI-Based Classification of Pediatric Breath Sounds: Toward a Tool for Early Respiratory Screening
## 1 Introduction  
Respiratory diseases, including community-acquired pneumonia, asthma, and acute asthma exacerbations, are a major source of morbidity and mortality in children, with the World Health Organization (WHO) identifying acute respiratory infections as the leading cause of death in children under five years of age [1, 2, 3]. In the United States, 6.3 million children are diagnosed with asthma. Most respiratory tract infections (RTIs) are self-limiting, and they impose a significant financial burden, costing billions annually inambulatory care [4; 5; 6; 7]. While diagnostic tests such as chest radiographs and assessments of vital signs and symptoms are essential, auscultation and clinical observation remain critical in diagnosing pediatric respiratory conditions. Misdiagnosis or misinterpretation in this area can lead to unnecessary pressure on healthcare resources. The evaluation of bodily sounds dates back to ancient Egypt [5]. A major milestone occurred in 1816 when Dr. [PERSON] invented the first stethoscope using a rolled sheet of paper [8]. This innovation quickly became a typical assessment method in clinical practice, and the stethoscope has since remained a fundamental diagnostic tool [8]. Lung auscultation, in particular, continues to be a vital component of respiratory assessments due to its accessibility, affordability, and clinical relevance [9]. However, despite its advantages, chest auscultation suffers from significant inter-listener variability. It requires the overall pooled sensitivity for lung auscultation to be 37% and specificity 89% [10], which limits its diagnostic reliability [8]. To address these limitations, in recent years, many researchers have tried to develop technology-assisted auscultation and have used computerized approaches to analyze breath and lung sounds [9; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Some studies have explored general respiratory sound analysis [9], while others have focused on specific conditions such as chronic obstructive pulmonary disease (COPD) [11]. For instance, [PERSON] and [PERSON] developed methods to analyze wheeze signals in asthma patients [11], and [PERSON] et al. introduced an artificial intelligence (AI)-based algorithm to predict bronchopulmonary dysplasia in newborns, aiming to improve clinical outcomes. Additionally, computerized lung sound analysis tools have been proposed to support disease diagnosis [16; 17]. [PERSON] developed a system using computerized analysis to classify respiratory pathologies from lung sounds [25], distinct from broader breath signals, while [PERSON] applied hidden Markov models (HMMs) to automate breath sound classification for sleep-disordered breathing [26]. These approaches are tailored to adult conditions like chronic obstructive pulmonary disease (COPD) [11], asthma [5; 27], pneumonia [27], and bronchopulmonary dysplasia [11; 28]. Since respiratory sounds in children differ significantly from those in adults [9; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29], these models are insufficient for early childhood applications [30]. Researchers also use machine learning to interpret children's lung sounds. [PERSON] and [PERSON] proposed the use of digital stethoscopes to detect pathological breath sounds in children [31]. [PERSON] and colleagues conducted research on deep learning models for detecting wheezing in pediatric patients [32]. [PERSON] and [PERSON] employed support vector machines (SVMs) to classify breath sounds in children, including distinctions between normal and abnormal, crackles and wheezing, normal and crackles, and normal and wheezing [33]. [PERSON] and [PERSON] also applied deep learning techniques to analyze breath sounds in children with asthma [34]. [PERSON] developed an automated procedure for analyzing lung sounds in children with a history of wheezing [35]. [PERSON] and [PERSON] utilized machine learning-based strategies to improve wheezing detection in pediatric populations [36].