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Review
. 2023 Jun 12;23(12):5514.
doi: 10.3390/s23125514.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Arshia K Sethi  1 , Pratyusha Muddaloor  1 , Priyanka Anvekar  2 , Joshika Agarwal  3 , Anmol Mohan  1 , Mansunderbir Singh  4 , Keerthy Gopalakrishnan  1   5 , Ashima Yadav  6 , Aakriti Adhikari  1 , Devanshi Damani  6   7 , Kanchan Kulkarni  8   9 , Christopher A Aakre  2 , Alexander J Ryu  2 , Vivek N Iyer  3 , Shivaram P Arunachalam  1   2   4   5
Affiliations
Review

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Arshia K Sethi et al. Sensors (Basel). .

Abstract

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Keywords: AI; auscultation; deep learning; electronic stethoscope; lung sounds; machine learning; phonopulmogram; respiratory disorders.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Physiological origin of lung sounds and primary sites of auscultation [30]. Numbers in the figure represent the points of auscultation.
Figure 2
Figure 2
Pictorial representation of various AI-assisted clinical applications of phonopulmograms [30].
Figure 3
Figure 3
Pictorial representation of digital phonopulmography using AI-assisted dual microwave sensing systems [30].
See this image and copyright information in PMC

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