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

Abstract Machine learning has proven to be an effective technique in recent years and
machine learning algorithms have been successfully used in a large number of applications …

Purpose Lung diseases are the third leading cause of death worldwide. Stethoscope-based
auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic …

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 …

The use of computer-based automated approaches and improvements in lung sound
recording techniques have made lung sound-based diagnostics even better and devoid of …

Artificial intelligence (AI) has recently been established as an alternative method to many
conventional methods. The implementation of AI techniques for respiratory sound analysis …

Objective: This paper describes the state of the art, scientific publications, and ongoing
research related to the methods of analysis of respiratory sounds. Methods and Material …

RATIONALE: The standardized use of a stethoscope for chest auscultation in clinical
research is limited by its inherent inter-listener variability. Electronic auscultation and …

With the development of computer-systems that can collect and analyze enormous volumes
of data, the medical profession is establishing several non-invasive tools. This work attempts …

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic
methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound …