Automated differential diagnostics of respiratory diseases using an electronic stethoscope

IF 0.7 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Polish Journal of Medical Physics and Engineering Pub Date : 2023-12-01 DOI:10.2478/pjmpe-2023-0022
Diana Arhypenko, Denis Panaskin, Dmytro Babko
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Abstract

Abstract Introduction: The outbreak of the coronavirus infection, which has escalated into a pandemic, has worsened the already unfavourable situation with respiratory system diseases in Ukraine. The burden on doctors has significantly increased, necessitating the exploration of simplified and expedited methods for conducting routine respiratory examinations. The research aims to describe a model for creating an automated differential diagnosis of respiratory noise using an electronic stethoscope, combining medical and clinical information about the types of respiratory noise characterizing the normal or pathological state of the respiratory system with a means of its information and technical processing. Material and methods: The research methods were analysis of theoretical information about the types of respiratory noise, analysis of technical information for choosing an information technology tool for processing biological signals; synthesis of the results; modelling. Results: The research resulted in a model of automated differential diagnosis based on the principle of auscultation, which includes the process of extracting the sound of air movement inside and outside the lungs and the classification of the extracted sounds. Automation of this process concerned only the classification of the extracted sounds since the principle of extraction itself was the same for both mechanical and automatic implementations. Conclusions: The automatic classification process was intended to reduce the time of the procedure and reduce the influence of the human factor, eliminating the possibility of medical error. To implement the process, a deep machine learning method was used, the array of information for which was to be a created phonotheque of acoustic signals of the respiratory system, which would include all types of respiratory noise concerning normal or pathological processes in the body.
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使用电子听诊器自动鉴别诊断呼吸系统疾病
摘要 导言:冠状病毒感染已升级为大流行病,它的爆发使乌克兰本已不利的呼吸系统疾病形势更加恶化。医生的负担明显加重,因此有必要探索简化和加快呼吸系统常规检查的方法。本研究旨在描述一种使用电子听诊器对呼吸噪音进行自动鉴别诊断的模式,该模式结合了有关呼吸系统正常或病理状态的呼吸噪音类型的医学和临床信息,以及信息和技术处理手段。材料和方法:研究方法是分析有关呼吸噪音类型的理论信息,分析选择处理生物信号的信息技术工具的技术信息;综合结果;建模。结果:研究得出了基于听诊原理的自动鉴别诊断模型,其中包括提取肺内外空气流动的声音以及对提取的声音进行分类的过程。这一过程的自动化只涉及提取声音的分类,因为机械和自动实施的提取原理本身是相同的。结论自动分类过程旨在缩短程序时间,减少人为因素的影响,消除医疗错误的可能性。为了实现这一过程,使用了一种深度机器学习方法,其信息阵列是一个已创建的呼吸系统声音信号图谱,其中包括与人体正常或病理过程有关的所有类型的呼吸噪音。
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来源期刊
Polish Journal of Medical Physics and Engineering
Polish Journal of Medical Physics and Engineering RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
1.30
自引率
0.00%
发文量
19
期刊介绍: Polish Journal of Medical Physics and Engineering (PJMPE) (Online ISSN: 1898-0309; Print ISSN: 1425-4689) is an official publication of the Polish Society of Medical Physics. It is a peer-reviewed, open access scientific journal with no publication fees. The issues are published quarterly online. The Journal publishes original contribution in medical physics and biomedical engineering.
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