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      The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine

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          Abstract

          Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence (AI) and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of paediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, AI-assisted auscultation, and the implications of these technologies on telemedicine in paediatric cardiology. It is our hope that this article provides an updated perspective on the impact of AI on cardiac auscultation for the modern paediatric cardiologist.

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          Most cited references31

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          The electronic stethoscope

          Most heart diseases are associated with and reflected by the sounds that the heart produces. Heart auscultation, defined as listening to the heart sound, has been a very important method for the early diagnosis of cardiac dysfunction. Traditional auscultation requires substantial clinical experience and good listening skills. The emergence of the electronic stethoscope has paved the way for a new field of computer-aided auscultation. This article provides an in-depth study of (1) the electronic stethoscope technology, and (2) the methodology for diagnosis of cardiac disorders based on computer-aided auscultation. The paper is based on a comprehensive review of (1) literature articles, (2) market (state-of-the-art) products, and (3) smartphone stethoscope apps. It covers in depth every key component of the computer-aided system with electronic stethoscope, from sensor design, front-end circuitry, denoising algorithm, heart sound segmentation, to the final machine learning techniques. Our intent is to provide an informative and illustrative presentation of the electronic stethoscope, which is valuable and beneficial to academics, researchers and engineers in the technical field, as well as to medical professionals to facilitate its use clinically. The paper provides the technological and medical basis for the development and commercialization of a real-time integrated heart sound detection, acquisition and quantification system.
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            Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial.

            Artificial intelligence (AI) has potential to improve the accuracy of screening for valvular and congenital heart disease by auscultation. However, despite recent advances in signal processing and classification algorithms focused on heart sounds, clinical acceptance of this technology has been limited, in part due to lack of objective performance data. We hypothesized that a heart murmur detection algorithm could be quantitatively and objectively evaluated by virtual clinical trial. All cases from the Johns Hopkins Cardiac Auscultatory Recording Database (CARD) with either a pathologic murmur, an innocent murmur or no murmur were selected. The test algorithm, developed independently of CARD, analyzed each recording using an automated batch processing protocol. 3180 heart sound recordings from 603 outpatient visits were selected from CARD. Algorithm estimation of heart rate was similar to gold standard. Sensitivity and specificity for detection of pathologic cases were 93% (CI 90-95%) and 81% (CI 75-85%), respectively, with accuracy 88% (CI 85-91%). Performance varied according to algorithm certainty measure, age of patient, heart rate, murmur intensity, location of recording on the chest and pathologic diagnosis. This is the first reported comprehensive and objective evaluation of an AI-based murmur detection algorithm to our knowledge. The test algorithm performed well in this virtual clinical trial. This strategy can be used to efficiently compare performance of other algorithms against the same dataset and improve understanding of the potential clinical usefulness of AI-assisted auscultation.
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              Neural network classification of homomorphic segmented heart sounds

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                Author and article information

                Journal
                Eur Heart J Digit Health
                Eur Heart J Digit Health
                ehjdh
                European Heart Journal. Digital Health
                Oxford University Press
                2634-3916
                September 2021
                01 July 2021
                01 July 2021
                : 2
                : 3
                : 456-466
                Affiliations
                [1 ] Department of Pediatrics, Blalock Taussig Thomas Heart Center, The Johns Hopkins Hospital and School of Medicine , M2315, 1800 Orleans St, Baltimore, MD 21287, USA
                [2 ] Department of Cardiology and Pneumology, University Medical Center Göttingen, Georg-August University , Göttingen, Germany
                [3 ] German Center for Cardiovascular Research (DZHK), Partner Site Göttingen , 37077 Göttingen, Germany
                Author notes
                Corresponding author. Tel: +1 410 502 3350, Fax: +1 410 955 9897, Email: skutty1@ 123456jhmi.edu
                Author information
                https://orcid.org/0000-0001-9428-0979
                Article
                ztab059
                10.1093/ehjdh/ztab059
                9707892
                36713594
                3f7d1acd-041d-4eb3-b219-4d859f591b8e
                © The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 21 February 2021
                : 19 June 2021
                : 28 June 2021
                Page count
                Pages: 11
                Categories
                Point of View

                auscultation,artificial intelligence,congenital heart disease,paediatric murmurs

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