Review of advancements in AI-assisted lung sound analysis for respiratory illness diagnosis in noisy environments

Abstract

For several centuries, research has been carried out to address respiratory ailments, which are among the most detrimental to human health. The advent of the stethoscope in the 19th century has facilitated the identification of respiratory sounds. This innovation represents a significant advancement in the identification and diagnosis of numerous respiratory ailments. In Malaysia, public hospitals have traditionally employed stethoscopes in their emergency departments. However, the precision of readings obtained through this method is susceptible to interference from ambient noise, uneven terrain, and suboptimal acoustic performance, particularly during medical transportation. Consequently, this can result in erroneous diagnoses and inappropriate treatment. Potential remedies for addressing the challenges associated with assessing respiratory sounds during medical transportation include advancements in stethoscope technology, novel auditory techniques, and reduced levels of background noise within the transportation environment. The present investigation concerns the effects of developing a new machine learning (ML) algorithm for the assessment of lung sound in conditions of high ambient noise. The objective is to devise a ML algorithm that can categorize acute respiratory illnesses based on their level of urgency in the presence of ambient noise.