Auscultation, a cost-effective and non-invasive part of physical examination, is essential to
diagnose pediatric respiratory disorders. Electronic stethoscopes allow transmission …

Background Manual auscultation to detect abnormal breath sounds has poor inter-observer
reliability. Digital stethoscopes with artificial intelligence (AI) could improve reliable detection …

Recently, many studies were performed using several techniques to classify and diagnose
lung sound, but as a drawback the age category was limited, almost adult only, as well as …

The use of machine learning in biomedical research has surged in recent years thanks to
advances in devices and artificial intelligence. Our aim is to expand this body of knowledge …

Pulmonary auscultation is essential for detecting abnormal lung sounds during physical
assessments, but its reliability depends on the operator. Machine learning (ML) models offer …

(1) Background: Mastery of auscultation can be challenging for many healthcare providers.
Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the …

Purpose Lung auscultation has long been a standard of care for the diagnosis of respiratory
diseases. Recent advances in electronic auscultation and signal processing have yet to find …

Patients with respiratory conditions typically exhibit adventitious respiratory sounds (ARS),
such as wheezes and crackles. In recent years, computerized methods for analyzing …

Background Acute respiratory diseases are a leading cause of morbidity and mortality in
children. Cough is a common symptom of acute respiratory diseases and the sound of …

Auscultation is an important diagnostic method for lung diseases. However, it is a subjective
modality and requires a high degree of expertise. To overcome this constraint, artificial …