The use of wireless technology for thoracic physical examination: a pilot case based on a literature review
Copyright (c) 2023 Marco Umberto Scaramozzino, Giovanni Sapone, Ubaldo Romeo Plastina, Guido Levi , Mariacarmela Nucara , Maura Festa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Auscultation is a standard method of physical examination used by physicians and is widely accepted by doctors and patients for its simplicity, repeatability and non-invasiveness. Artificial intelligence is the 'new integrated frontier' of the thoracic examination, yet there are still concordance discrepancies in obstructive pulmonary diseases; on the contrary, for fibrotic diseases, the degree of concordance increases significantly, as shown by previous clinical studies conducted mainly in children. However, there are data in the literature that appear to be very discordant on certain types of lung noises, such as wet crackles and dry noises; therefore, the application of these devices in daily use in outpatient and hospital settings needs to be further expanded. The integrated data allowed us to make the right diagnosis, also avoiding costs for the national health system and possible invasive procedures such as bronchoscopy, which today remains the “gold standard” for the histological diagnosis of sarcoidosis with lung localisation. Integrated technology could improve the diagnostic capacity in restrictive lung diseases, as shown in this clinical case. Several randomised controlled trials are still needed to increase the significance of this initial integrated research work performed
- Kevat AC, Kalirajah A, Roseby R. Digital stethoscopes compared to standard auscultation for detecting abnormal paediatric breath sounds. Eur J Pediatr. 2017 Jul;176(7):989-992. doi: 10.1007/s00431-017-2929-5
- Ohshimo S, Sadamori T, Tanigawa K. Innovation in Analysis of Respiratory Sounds. Ann Intern Med. 2016 May 3;164(9):638-9. doi: 10.7326/L15-0350.
- Reyes BA, Olvera-Montes N, Charleston-Villalobos S. et al. A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients. Sensors (Basel). 2018 Nov 7;18(11):3813. doi: 10.3390/s18113813.
- Rennoll V, McLane I, Emmanouilidou D et al. Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope. IEEE J Biomed Health Inform. 2021 May;25(5):1542-1549. doi: 10.1109/JBHI.2020.3020494.
- Pasterkamp H, Kraman SS, Wodicka GR. Respiratory sounds. Advances beyond the stethoscope. Am J Respir Crit Care Med. 1997 Sep;156(3 Pt 1):974-87. doi: 10.1164/ajrccm.156.3.9701115.
- Böhme, H. R. 1974. Attempt at physical characterization of the passive sound behavior in the lung in a model. Z. Gesamte Inn. Med. 29:401–406.
- Kevat A, Kalirajah A, Roseby R. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Respir Res. 2020 Sep 29;21(1):253. doi: 10.1186/s12931-020-01523-09.
- Challen R, Denny J, Pitt M, et al. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28(3):231–7.
- Bertrand Z F, Segall K D, Sánchez D I, et al. La auscultación pulmonar en el siglo 21 [Lung auscultation in the 21th century]. Rev Chil Pediatr. 2020 Aug;91(4):500-506. Spanish. doi: 10.32641/rchped. v91i4.1465.
- Palaniappan R, Sundaraj K, Sundaraj S. Artificial intelligence techniques used in respiratory sound analysis--a systematic review. Biomed Tech (Berl). 2014 Feb;59(1):7-18. doi: 10.1515/bmt-2013-0074.
- Grzywalski T, Piecuch M, Szajek M, et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. Eur J Pediatr. 2019 Jun;178(6):883-890. doi: 10.1007/s00431-019-03363-2.
- Ye P, Li Q, Jian W, et al. Regularity and mechanism of fake crackle noise in an electronic stethoscope. Front Physiol. 2022 Dec 12; 13:1079468. doi: 10.3389/fphys.2022.1079468. PMID: 36579022.
- Andrès E, Gass R, Charloux A, et al. Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0. J Med Life. 2018 Apr-Jun;11(2):89-106. PMID: 30140315.
- Olvera-Montes N, Reyes B, Charleston-Villalobos S, et al. Detection of Respiratory Crackle Sounds via an Android Smartphone-based System. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:1620-1623. doi: 10.1109/EMBC.2018.8512672.
- Andrews E, Hayes A, Cerulli L, et al. Legacy Building in Pediatric End-of-Life Care through Innovative Use of a Digital Stethoscope. Palliat Med Rep. 2020 Aug 6;1(1):149-155. doi: 10.1089/pmr.2020.0028.
- Kim Y, Hyon Y, Jung SS, Lee S, et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep. 2021 Aug 25;11(1):17186. doi: 10.1038/s41598-021-96724-7.
- Kim Y, Hyon Y, Lee S, et al. The coming era of a new auscultation system for analyzing respiratory sounds. BMC Pulm Med. 2022 Mar 31;22(1):119. doi: 10.1186/s12890-022-01896-1.
- Zhang J, Wang HS, Zhou HY, et al. Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children. Front Pediatr. 2021 Mar 23; 9:627337. doi: 10.3389/fped.2021.627337.
- Behere S, Baffa JM, Penfil S, et al. Real-World Evaluation of the Eko Electronic Teleauscultation System. Pediatr Cardiol. 2019 Jan;40(1):154-160. doi: 10.1007/s00246-018-1972-y.
- Zhang P, Wang B, Liu Y, et al. Lung Auscultation of Hospitalized Patients with SARS-CoV-2 Pneumonia via a Wireless Stethoscope. Int J Med Sci. 2021 Jan 28;18(6):1415-1422. doi: 10.7150/ijms.54987.
- Horimasu Y, Ohshimo S, Yamaguchi K, et al. A machine-learning based approach to quantify fine crackles in the diagnosis of interstitial pneumonia: A proof-of-concept study. Medicine (Baltimore). 2021 Feb 19;100(7):e24738. doi: 10.1097/MD.0000000000024738.