Real-World Evaluation of the Eko Electronic Teleauscultation System

Behere, Shashank; Baffa, Jeanne M.; Penfil, Scott; Slamon, Nicholas

doi:10.1007/s00246-018-1972-y

Cited by 24 publications

(20 citation statements)

References 15 publications

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“…To overcome the aforementioned limitations, computerized respiratory sound analysis [12] is required. Furthermore, a tele-auscultation system [13,14] with automated respiratory sound analysis can be realized in the form of a mobile app or web service with proper infrastructure supports, which can facilitate remote respiratory monitoring not only in a clinical setting but in a homecare setting. The tele-auscultation system can greatly help during care of COVID-19 patients.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Hsu

Huang²,

Huang³

et al. 2021

PLoS ONE

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A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios—such as in monitoring disease progression of coronavirus disease 2019—to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

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Section: Introductionmentioning

confidence: 99%

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Hsu

Huang²,

Huang³

et al. 2021

PLoS ONE

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“…29 Digital stethoscopes can record, analyse and send heart sounds to the clinician to diagnose heart murmurs or pulmonary hypertension. 30,31 Point-of-care ultrasounds are now connected to smartphones and are more accessible than ever with decreasing costs.…”

Section: Cardiovascularmentioning

confidence: 99%

The virtual physical exam in the 21st century

2019

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The modern Western medical encounter follows a strict framework that weaves subjective and objective components into a unifying diagnosis. As health care changes to incorporate new technology, such as virtual health care, the components that lead to diagnosis must likewise evolve. The virtual physical exam has limitations compared with the traditional exam. Despite this limitation, every year more patients are seen virtually with high satisfaction. Data have shown that supplementary real-time patient–provider video telemedicine increases access and extends established patient–physician relationships which will likely fuel increased telemedicine adoption even further. However, to date, there are limited data regarding the validity of the virtual examination compared with the traditional physical exam. In this paper, we review the use of developing technology related to the virtual physical exam.

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“…Only one study has utilised this device to auscultate various murmur types and compare the accuracy of physician diagnosis using traditional auscultation methods compared to the DS .…”

Section: M Littman Electronic Model 3200/4000mentioning

confidence: 99%

Digital stethoscopes in paediatric medicine

et al. 2019

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Aim: To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine.Methods: Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases.Results: Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics. Conclusion: Further validation and testing of available DS devices is required. Comparisonstudies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.

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Real-World Evaluation of the Eko Electronic Teleauscultation System

Cited by 24 publications

References 15 publications

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database—HF_Lung_V1

The virtual physical exam in the 21st century

Digital stethoscopes in paediatric medicine

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