Evidence map›Paper›PMID 37592751›Full record

ArticleTuberculosis and respiratory diseases2023

Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices.

Yoonjoo Kim, YunKyong Hyon, Seong-Dae Woo, Sunju Lee, Song-I Lee, Taeyoung Ha, Chaeuk Chung

Abstract read
In one paragraph

Article in Tuberculosis and respiratory diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yoonjoo KimDivision of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea.
YunKyong HyonDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Seong-Dae WooDivision of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea.
Sunju LeeDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Song-I LeeDivision of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea.
Taeyoung HaDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Chaeuk ChungDivision of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea.

Funding

National Institute for Mathematical Sciences B23910000National Research Foundation of Korea 2022R1F1A1076515
6 · The paper itself

Abstract

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

Indexed as

Artificial IntelligenceMachine LearningStethoscopeWearable DeviceWireless Technology

Identifiers

PMID37592751
PMCPMC10555525

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.