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      Digital Stethoscope Use in Neonates: A Systematic Review

      review-article

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          ABSTRACT

          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.

          How to cite this article

          Roff M, Slifirski O, Grooby E, et al. Digital Stethoscope Use in Neonates: A Systematic Review. Newborn 2023;2(3):235–243.

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          Most cited references53

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          Matthew J L Page, Joanne E McKenzie, Patrick Bossuyt … (2021)
          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
          Article 0 comments Cited 22218 times – based on 0 reviews     Review now
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            Artificial intelligence: A powerful paradigm for scientific research

            Yongjun Xu, Xin Liu, Xin Cao … (2021)
            Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
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              Computational bioacoustics with deep learning: a review and roadmap

              Dan Stowell (2022)
              Animal vocalisations and natural soundscapes are fascinating objects of study, and contain valuable evidence about animal behaviours, populations and ecosystems. They are studied in bioacoustics and ecoacoustics, with signal processing and analysis an important component. Computational bioacoustics has accelerated in recent decades due to the growth of affordable digital sound recording devices, and to huge progress in informatics such as big data, signal processing and machine learning. Methods are inherited from the wider field of deep learning, including speech and image processing. However, the tasks, demands and data characteristics are often different from those addressed in speech or music analysis. There remain unsolved problems, and tasks for which evidence is surely present in many acoustic signals, but not yet realised. In this paper I perform a review of the state of the art in deep learning for computational bioacoustics, aiming to clarify key concepts and identify and analyse knowledge gaps. Based on this, I offer a subjective but principled roadmap for computational bioacoustics with deep learning: topics that the community should aim to address, in order to make the most of future developments in AI and informatics, and to use audio data in answering zoological and ecological questions.
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                Author and article information

                Journal
                Journal ID (publisher-id): JNB
                Title: Newborn
                Abbreviated Title: JNB
                Publisher: Jaypee Brothers Medical Publishers
                ISSN (Electronic): 2769-514X
                Publication date (Print): July-September 2023
                Volume: 2
                Issue: 3
                Pages: 235-243
                Affiliations
                [1 ]Department of Paediatrics, Monash University, Melbourne, Australia
                [2 ]Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, Australia
                [3 ]Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, British Columbia, Canada
                [4 ]Monash Newborn, Monash Children's Hospital, Melbourne, Australia
                Author notes
                Atul Malhotra, Department of Paediatrics, Monash University, Melbourne, Australia, Phone: +61 385723650, e-mail: atul.malhotra@ 123456monash.edu
                Article
                DOI: 10.5005/jp-journals-11002-0068
                SO-VID: 96ed092a-46c1-499c-9978-e716fdd032ab
                Copyright © Copyright © 2023; The Author(s).
                License:

                © The Author(s). 2023 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                Date received : 14 June 2023
                Date accepted : 22 July 2023
                Date: 25 September 2023
                Categories
                Subject: REVIEW ARTICLE
                Custom metadata
                file-pdf jnb-02-235.pdf

                ScienceOpen disciplines: Pediatrics
                Keywords: Machine learning,Murmur detection,Newborn,Phonocardiography,Respiratory distress,Telemedicine,Artificial Intelligence,Auscultation,Computer-assisted auscultation,Infant
                Data availability:
                ScienceOpen disciplines: Pediatrics
                Keywords: Machine learning, Murmur detection, Newborn, Phonocardiography, Respiratory distress, Telemedicine, Artificial Intelligence, Auscultation, Computer-assisted auscultation, Infant

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