References
- Montinari MR, Minelli S. The first 200 years of cardiac auscultation and future perspectives. J Multidiscip Healthc. 2019;12:183–189.
- Clark D, Ahmed MI, Dell’italia LJ, et al. An argument for reviving the disappearing skill of cardiac auscultation. Cleve Clin J Med. 2012;79(8):536–537, 544.
- Ramanathan A, Zhou L, Marzbanrad F, et al. Digital stethoscopes in paediatric medicine. Acta Paediatr. 2019;108(5):814–822.
- March SK. W. proctor harvey: a master clinician-teacher’s influence on the history of cardiovascular medicine. Texas Heart Institute J. 2002;29(3):182.
- MobilDrTech. White paper: Telemedicine stethoscopes; 2020. Available from: https://ww1.prweb.com/prfiles/2020/07/07/17242265/Telemedicine%20Stethoscopes%20White%20Paper%20MDrT%207-5-2020.pdf.
- Judge R, Mangrulkar R. Heart sound and murmur library2015. Available from: https://open.umich.edu/find/open-educational-resources/medical/heart-sound-murmur-library.
- Neomed library, physical diagnosis: sounds, videos, and images; 2002. Available from: https://libraryguides.neomed.edu/physicaldiagnosis/auscultation.
- Thinklabs. Thinklabs auscultation sounds library; 2022. Available from: https://www.thinklabs.com/sound-library.
- Tavel ME. Cardiac auscultation: a glorious past—but does it have a future? Circulation. 1996;93(6):1250–1253.
- Mangione S. Cardiac auscultatory skills of physicians-in-training: a comparison of three english-speaking countries. Am J Med. 2001;110(3):210–216.
- Vukanovic-Criley JM, Criley S, Warde CM, et al. Competency in cardiac examination skills in medical students, trainees, physicians, and faculty: a multicenter study. Arch Intern Med. 2006;166(6):610–616.
- Mehmood M, Grara HLA, Stewart JS, et al. Comparing the auscultatory accuracy of health care professionals using three different brands of stethoscopes on a simulator. Med Dev. 2014;7:273.
- Ohshimo S, Sadamori T, Tanigawa K. Innovation in analysis of respiratory sounds. Ann Intern Med. 2016;164(9):638–639.
- Shekhar R, Vanama G, John T, et al. Automated identification of innocent Still's murmur using a convolutional neural network. Front Pediatr. 2022;10:923956.
- Pyles L, Hemmati P, Pan J, et al. Initial field test of a cloud-based cardiac auscultation system to determine murmur etiology in rural China. Pediatr Cardiol. 2017;38(4):656–662.
- Thompson WR, Reinisch AJ, Unterberger MJ, et al. Artificial intelligence-assisted auscultation of heart murmurs: validation by virtual clinical trial. Pediatr Cardiol. 2019;40(3):623–629. http://www.ncbi.nlm.nih.gov/pubmed/30542919.
- Chorba JS, Shapiro AM, Le L, et al. Deep learning algorithm for automated cardiac murmur detection via a digital stethoscope platform. J Am Heart Assoc. 2021;10(9):e019905.
- Lv J, Dong B, Lei H, et al. Artificial intelligence-assisted auscultation in detecting congenital heart disease. Europ Heart J. 2021;2:119–124.
- Kevat A, Kalirajah A, Roseby R. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Respiratory Res. 2020;21(1):1–6.
- Liu J, Wang H, Yang Z, et al. Deep learning-based computer-aided heart sound analysis in children with left-to-right shunt congenital heart disease. Int J Cardiol. 2022;348:58–64.
- Panah DS, Hines A, Mckeever S. Exploring composite dataset biases for heart sound classification. In AICS 2020: 28th Irish Conference on Artificial Intelligence and Cognitive Science; Technological University Dublin; 2020. p. 145–156.
- Humayun AI, Ghaffarzadegan S, Ansari MI, et al. Towards domain invariant heart sound abnormality detection using learnable filterbanks. IEEE J Biomed Health Inform. 2020;24(8):2189–2198.
- Potes C, Parvaneh S, Rahman A, et al. Ensemble of feature-based and deep learning-based classifiers for detection of abnormal heart sounds. In 2016 computing in cardiology conference (CinC); IEEE; 2016. p. 621–624.
- Chen W, Sun Q, Chen X, et al. Deep learning methods for heart sounds classification: a systematic review. Entropy. 2021;23(6):667.
- Oliynyk V. Determination of the amplitude-frequency characteristic of the 3M Littmann 3200 electronic stethoscope. Acoustic Bull. 2013;16(3):46–57.
- Weiss D, Erie C, Butera IJ, et al. An in vitro acoustic analysis and comparison of popular stethoscopes. Med Devices. 2019;12:41–52.
- XTUGA. Xtuga recording isolation; 2022. Available from: https://www.amazon.com/XTUGA-Recording-Microphone-Isolation-Soundproof/dp/B096FPZPYL?th=1.
- Shanthakumari G, Priya E. Spectrogram-based detection of crackles from lung sounds. In 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT); IEEE; 2022. p. 1–6.