TY - JOUR
T1 - Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence
T2 - Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging
AU - Sethi, Arshia K.
AU - Muddaloor, Pratyusha
AU - Anvekar, Priyanka
AU - Agarwal, Joshika
AU - Mohan, Anmol
AU - Singh, Mansunderbir
AU - Gopalakrishnan, Keerthy
AU - Yadav, Ashima
AU - Adhikari, Aakriti
AU - Damani, Devanshi
AU - Kulkarni, Kanchan
AU - Aakre, Christopher A.
AU - Ryu, Alexander J.
AU - Iyer, Vivek N.
AU - Arunachalam, Shivaram P.
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/6
Y1 - 2023/6
N2 - 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.
AB - 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.
KW - AI
KW - auscultation
KW - deep learning
KW - electronic stethoscope
KW - lung sounds
KW - machine learning
KW - phonopulmogram
KW - respiratory disorders
UR - http://www.scopus.com/inward/record.url?scp=85163967632&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163967632&partnerID=8YFLogxK
U2 - 10.3390/s23125514
DO - 10.3390/s23125514
M3 - Review article
C2 - 37420680
AN - SCOPUS:85163967632
SN - 1424-8220
VL - 23
JO - Sensors
JF - Sensors
IS - 12
M1 - 5514
ER -