There is both a variety of terms used for the same sound by different doctors and different sounds described by the same term. This problem is widely recognized, but to date, there is still no standardized worldwide classification of the types of phenomena appearing in the respiratory system. Another important issue is the inconsistent nomenclature of respiratory sounds. However, the results of such examinations are strongly subjective and cannot be shared and communicated easily, mostly because of doctors’ experience and perceptual abilities, which leads to differences in the their assessments, depending on their specialization (Hafke et al., submitted for publication). The stethoscope still remains a tool that can provide potentially valuable clinical information. The stethoscope, introduced by Laennec more than two centuries ago, was one of the first medical instruments which enabled internal body structures and their functioning to be checked. The gathered data show that machine learning (ML)–based analysis is more efficient in detecting all four types of phenomena, which is reflected in high values of recall (also called as sensitivity) and F1-score.Ĭonclusions: The obtained results suggest that the implementation of automatic sound analysis based on NNs can significantly improve the efficiency of this form of examination, leading to a minimization of the number of errors made in the interpretation of auscultation sounds.Īuscultation has been considered as an integral part of physical examination since the time of Hippocrates.
In the blind test, a group of 522 auscultatory sounds from 50 pediatric patients were presented, and the results provided by a group of doctors and an artificial intelligence (AI) algorithm developed by the authors were compared. It allows the detection of auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles.
This paper investigates a new method of automatic sound analysis based on neural networks (NNs), which has been implemented in a system that uses an electronic stethoscope for capturing respiratory sounds. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. However, its biggest drawback is its subjectivity. Lung auscultation is an important part of a physical examination.
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