Aim: To assess the evidence for the use of digital stethoscopes in neonates and evaluate whether they are effective, appropriate, and advantageous for neonatal auscultation.
Methods: A systematic review and narrative synthesis of studies published between January 1, 1990 and May 29, 2023 was conducted following searches of MEDLINE, Embase, PubMed, Scopus, and Google Scholar databases, as well as trial registries.
Results: Of 3,852 records identified, a total of 41 papers were eligible and included in the narrative synthesis. Thirteen records were non-full-text articles, either in the form of journal letters or conference abstracts, and these were included separately for completion purposes but may be unreliable. Twenty eight papers were full-text articles and were included in a full qualitative analysis. Digital stethoscopes have been studied in neonatology across various clinical areas, including artificial intelligence for sound quality assessment and chest sound separation (n = 5), cardiovascular sounds (n = 11), respiratory sounds (n = 4), bowel sounds (n = 4), swallowing sounds (n = 2), and telemedicine (n = 2). This paper discusses the potential utility of digital stethoscope technology for the interpretation of neonatal sounds for both humans and artificial intelligence. The limitations of current devices are also assessed.
Conclusions: The utilization of digital stethoscopes in neonatology is an emerging field with a wide range of potential applications, which has the capacity to advance neonatal auscultation. Artificial intelligence and digital stethoscope technology offer novel objective avenues for automatic pathological sound detection. Further, digital stethoscopes may improve our scientific understanding of normal neonatal physiology and can be employed in telemedicine to facilitate remote medical access. Digital stethoscopes can also provide phonocardiograms, enabling enhanced interpretation of neonatal cardiac sounds. However, current digital stethoscopes necessitate refinement as they consistently produce low-quality sounds when used on neonates.
Permin H, Norn S. Stethoscope – Over 200 years. J Pulmonol Respir Res 2019;3:001–008. DOI: 10.29328/journal.jprr.1001010.
Harbison J. ‘The old guessing tube’: 200 years of the stethoscope. QJM: An Int J Med 2017;110(1):9–10. DOI: 10.1093/qjmed/hcw108.
3M Littmann. 3M Littmann Classic II Infant Stethoscope [cited 2023 March 28]. Available from: https://www.littmann.com.au/3M/en_AU/littmann-stethoscopes-au/products/~/3M-Littmann-Classic-II-Infant-Stethoscope/?N=5932256+8711017+3290700351+3294857444&rt=rud.
Hafke-Dys H, Bręborowicz A, Kleka P, et al. The accuracy of lung auscultation in the practice of physicians and medical students. PLoS One 2019;14(8):e0220606. DOI: 10.1371/journal.pone.0220606.
Bank I, Vliegen HW, Bruschke AV. The 200th anniversary of the stethoscope: Can this low-tech device survive in the high-tech 21st century? Eur Heart J 2016;37(47):3536–3543. DOI: 10.1093/eurheartj/ehw034.
Richardson TR, Moody JM. Bedside cardiac examination: Constancy in a sea of change. Curr Probl Cardiol 2000;25(11):783–825. DOI: 10.1067/mcd.2000.109835.
Zun LS, Downey L. The effect of noise in the emergency department. Acad Emerg Med 2005;12(7):663–666. DOI: 10.1197/j.aem.2005.03.533.
Arts L, Lim EHT, van de Ven PM, et al. The diagnostic accuracy of lung auscultation in adult patients with acute pulmonary pathologies: A meta-analysis. Sci Rep 2020;10(1):7347. DOI: 10.1038/s41598-020-64405-6.
Grooby E, He J, Kiewsky J, et al. Neonatal heart and lung sound quality assessment for robust heart and breathing rate estimation for telehealth applications. IEEE J Biomed Health Inform 2021;25(12): 4255–4266. DOI: 10.1109/JBHI.2020.3047602.
Grooby E, Sitaula C, Fattahi D, et al. Real-time multi-level neonatal heart and lung sound quality assessment for telehealth applications. IEEE Access 2022;10:10934–10948. DOI: 10.1109/ACCESS.2022.3144355.
Fattahi D, Sameni R, Grooby E, et al. A blind filtering framework for noisy neonatal chest sounds. IEEE Access 2022;10:50715–50727. DOI: 10.1109/ACCESS.2022.3170052.
Grooby E, Sitaula C, Fattahi D, et al. Noisy neonatal chest sound separation for high-quality heart and lung sounds. IEEE J Biomed Health Inform 2023;27(6):2635–2646. DOI: 10.1109/JBHI.2022.3215995.
Elphick HE, Lancaster GA, Solis A, et al. Validity and reliability of acoustic analysis of respiratory sounds in infants. Arch Dis Child 2004;89(11):1059–1063. DOI: 10.1136/adc.2003.046458.
Swarup S, Makaryus AN. Digital stethoscope: Technology update. Med Devices (Auckl) 2018;11:29–36. DOI: 10.2147/MDER.S135882.
Tavel ME. Cardiac auscultation: A glorious past--and it does have a future! Circulation 2006;113(9):1255–1259. DOI: 10.1161/CIRCULATIONAHA.105.591149.
Ghanayim T, Lupu L, Naveh S, et al. Artificial intelligence-based stethoscope for the diagnosis of aortic stenosis. Am J Med 2022;135(9):1124–1133. DOI: 10.1016/j.amjmed.2022.04.032.
Eko Health Inc. Eko AI Validation White Paper; 2020 [cited 2023 March 29]. Available from: https://uploads-ssl.webflow.com/5fca50c07c4b1314fe246a86/6247c228d81d9f7823c752c1_Eko%20AI%20White%20Paper%20-%20LBL105B.pdf.
Eko Health Inc. Eko App [cited 2023 March 29]. Available from: https://www.ekohealth.com/pages/smart-stethoscope-app.
3M Littmann. 3M Littmann CORE Digital Stethoscope [cited 2023 March 29]. Available from: https://www.littmann.com/3M/en_US/littmann-stethoscopes/advantages/core-digital-stethoscope/.
3M Littmann. 3M Littmann Electronic Stethoscope Model 3200 [cited 2023 March 29]. Available from: https://www.littmann.com.au/3M/en_AU/littmann-stethoscopes-au/products/~/3M-Littmann-Electronic-Stethoscope-Model-3200/?N=5142935+8711017+3290263838+3294857444&preselect=5002684+3293786499&rt=rud.
Design and Industry. Clini Cloud Digital Stethoscope [cited 2023 March 29]. Available from: https://www.design-industry.com.au/clinicloud.
Eko Health Inc. Eko DUO ECG + Digital Stethoscope [cited 2023 March 29]. Available from: https://www.ekohealth.com/products/duo-ecg-digital-stethoscope?variant=39350415655008.
Eko Health Inc. Eko CORE Digital Attachment [cited 2023 March 29]. Available from: https://www.ekohealth.com/products/core-digital-attachment?variant=32764121251936.
Thinklabs. Thinklabs One Digital Stethoscope [cited 2023 March 29]. Available from: https://www.thinklabs.com/.
Ramanathan A, Zhou L, Marzbanrad F, et al. Digital stethoscopes in paediatric medicine. Acta Paediatr 2019;108(5):814–822. DOI: 10.1111/apa.14686.
Covidence Systematic Review Software. Veritas Health Innovation, Melbourne, Australia. Available from: www.covidence.org.
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021;372:n71. DOI: 10.1136/bmj.n71.
Grooby E, He J, Fattahi D, et al. A new non-negative matrix co-factorisation approach for noisy neonatal chest sound separation. Annu Int Conf IEEE Eng Med Biol Soc 2021:5668–5673. DOI: 10.1109/EMBC46164.2021.9630256.
Yang X, Zeng W. A relative value method for measuring and evaluating neonatal cardiac reserve. Indian J Pediatr 2010;77(6):661–664. DOI: 10.1007/s12098-010-0058-5.
Balogh ÁTK, Kovács F. Application of phonocardiography on preterm infants with patent ductus arteriosus. Biomed Sign Process Control 2011;6(4):337–345. DOI: 10.1016/j.bspc.2011.05.009.
Sung P-H, Wang J-N, Chen B-W, et al. Auditory-inspired heart sound temporal analysis for patent ductus arteriosus. In: 2013 1st International Conference on Orange Technologies (ICOT) 2013. pp. 231–234.
Amiri AM, Abtahi M, Constant N, et al. Mobile phonocardiogram diagnosis in newborns using support vector machine. Healthcare (Basel) 2017;5(1):16. DOI: 10.3390/healthcare5010016.
Shelevytska VA, Mavropulo TK. Computer-aided auscultation of hemodynamic disorders in preterm neonates. S World Journal 2018:22–27. DOI: 10.30888/2663-5712.2020-06-02-052.
Grgic-Mustafic R, Baik-Schneditz N, Schwaberger B, et al. Novel algorithm to screen for heart murmurs using computer-aided auscultation in neonates: A prospective single center pilot observational study. Minerva Pediatr 2019;71(3):221–228. DOI: 10.23736/S0026-4946.18.04974-5.
Bobillo-Perez S, Balaguer M, Jordan I, et al. Delivery room ultrasound study to assess heart rate in newborns: DELIROUS study. Eur J Pediatr 2021;180(3):783–790. DOI: 10.1007/s00431-020-03776-4.
Gomez-Quintana S, Shelevytsky I, Shelevytska V, et al. Automatic segmentation for neonatal phonocardiogram. Annu Int Conf IEEE Eng Med Biol Soc 2021:135–138. DOI: 10.1109/EMBC46164.2021. 9630574.
Gómez-Quintana S, Schwarz CE, Shelevytsky I, et al. A Framework for AI-assisted detection of patent ductus arteriosus from neonatal phonocardiogram. Healthcare (Basel) 2021;9(2):169. DOI: 10.3390/healthcare9020169.
Takahashi K, Ono K, Arai H, et al. Detection of pathologic heart murmurs using a piezoelectric sensor. Sensors (Basel) 2021;21(4):1376. DOI: 10.3390/s21041376.
Amiri A, Armano G, Ghasemi S. Neonatal heart disease screening using an ensemble of decision trees. Int J Biomed Eng Technol 2022;39(2):107–130. DOI: 10.1504/IJBET.2022.124014.
Blowes RW, Yiallouros P, Milner AD. Lung sounds in neonates with and without an added dead space. Pediatr Pulmonol 1995;19(6):348–354. DOI: 10.1002/ppul.1950190607.
Ramanathan A, Marzbanrad F, Tan K, et al. Assessment of breath sounds at birth using digital stethoscope technology. Eur J Pediatr 2020;179(5):781–789. DOI: 10.1007/s00431-019-03565-8.
Zhou L, Marzbanrad F, Ramanathan A, et al. Acoustic analysis of neonatal breath sounds using digital stethoscope technology. Pediatr Pulmonol 2020;55(3):624–630. DOI: 10.1002/ppul.24633.
Grooby E, Sitaula C, Tan K, et al. Prediction of neonatal respiratory distress in term babies at birth from digital stethoscope recorded chest sounds. Annu Int Conf IEEE Eng Med Biol Soc 2022;2022: 4996–4999. DOI: 10.1109/EMBC48229.2022.9871449.
Song I, Huang Y, Koh THHG, et al. Pervasive monitoring of gastrointestinal health of newborn babies. In: Pham DN, Theeramunkong T, Governatori G, eds. PRICAI 2021: Trends in Artificial Intelligence. Springer, Cham; 2021, vol. 13031. pp. 359–369.
Sitaula C, He J, Priyadarshi A, et al. Neonatal bowel sound detection using convolutional neural network and laplace hidden semi-Markov model. IEEE/ACM Trans Audio, Speech, Lang Process 2022;30: 1853–1864. DOI: 10.1109/TASLP.2022.3178225.
Burne L, Sitaula C, Priyadarshi A, et al. Ensemble approach on deep and handcrafted features for neonatal bowel sound detection. IEEE J Biomed Health Inform 2022;27(6):2603–2613. DOI: 10.1109/JBHI.2022.3217559.
Zhou P, Lu M, Chen P, et al. Feasibility and basic acoustic characteristics of intelligent long-term bowel sound analysis in term neonates. Front Pediatr 2022;10:1000395. DOI: 10.3389/fped.2022.1000395.
Da Nobrega L, Boiron M, Henrot A, et al. Acoustic study of swallowing behaviour in premature infants during tube-bottle feeding and bottle feeding period. Early Hum Dev 2004;78(1):53–60. DOI: 10.1016/j.earlhumdev.2004.03.008.
Ince DA, Ecevit A, Acar BO, et al. Noninvasive evaluation of swallowing sound is an effective way of diagnosing feeding maturation in newborn infants. Acta Paediatr 2014;103(8):e340–e348. DOI: 10.1111/apa.12686.
Garingo A, Friedlich P, Tesoriero L, et al. The use of mobile robotic telemedicine technology in the neonatal intensive care unit. J Perinatol 2012;32(1):55–63. DOI: 10.1038/jp.2011.72.
Umoren RA, Gray MM, Handley S, et al. In-hospital telehealth supports care for neonatal patients in strict isolation. Am J Perinatol 2020;37(8):857–860. DOI: 10.1055/s-0040-1709687.
Stowell D. Computational bioacoustics with deep learning: A review and roadmap. Peer J 2022;10:e13152. DOI: 10.7717/peerj.13152.
Xu Y, Liu X, Cao X, et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation (Camb) 2021;2(4):100179. DOI: 10.1016/j.xinn.2021.100179.