使用基于心肺声音的深度学习来非侵入性检测腺体缩
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
这项研究引入了一种非侵入性深度学习方法,使用心肺声音来检测儿童的腺体缩. 该方法为这种常见的呼吸道疾病提供了一个具有成本效益和可访问的查工具.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 儿科呼吸系统医学 儿科呼吸系统医学
背景情况:
- 腺体缩是一种普遍存在的儿童上呼吸道疾病,引起鼻塞和睡眠呼吸暂停等症状.
- 目前的诊断方法 (CT扫描,内镜) 是侵入性的,使用辐射,不适合持续监测.
- 临床上需要使用非侵入性,可访问的方法来诊断和监测腺缩症.
研究的目的:
- 开发和验证一种新的深度学习方法,以使用心肺声来非侵入性检测腺体缩.
- 探索来自心肺声音的声信号与腺体大小之间的相关性.
- 建立一个深度学习框架,用于分类腺体缩严重程度和预测腺体大小.
主要方法:
- 创建了一个心肺声音数据库,标记数据将声音与腺体大小相关联.
- 实施了三个深度学习任务:二元分类 (正常与异常),四级分类 (严重程度) 和回归 (大小预测).
- 模型经过训练和评估,以评估它们在从声学数据中检测腺增大时的有效性.
主要成果:
- 深度学习模型在预测基于心肺声音的腺体缩方面表现出显著的有效性.
- 提出的方法准确地分类了严重程度,并预测了腺的尺寸.
- 这种方法显示出可靠的,非侵入性评估腺缩的希望.
结论:
- 对心肺声音的深度学习分析提供了一种可行的非侵入性方法来检测腺体缩.
- 这种方法可以简化诊断,降低医疗保健成本,并在资源有限的环境中实现远程自我查.
- 这些发现支持声学监测在儿童呼吸系统健康评估中的潜力.
相关概念视频
Assessment of Airway, Skin Color, and Use of Accessory Muscles
951
A thorough assessment of respiratory health is paramount in clinical settings to identify and manage respiratory distress and ensure adequate oxygenation. This article elaborates on the critical aspects of respiratory evaluation, including airway assessment, skin color examination, and the observation of accessory muscle use, which are integral to effectively diagnosing and managing patients with respiratory conditions.
Introduction
The initial evaluation of a patient's respiratory system...
Introduction
The initial evaluation of a patient's respiratory system...
951
Respiratory System Abnormal Finding II: Palpation and Auscultation
253
In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
253
Respiratory System Abnormal Finding I: Inspection and Percussion
195
Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
Inspection Findings
During an inspection, several findings may suggest the presence of respiratory distress or disease. Pursed-lip breathing, where exhalation is slowed by...
Inspection Findings
During an inspection, several findings may suggest the presence of respiratory distress or disease. Pursed-lip breathing, where exhalation is slowed by...
195
Physical Assessment of the Respiratory Tract IV: Auscultation
255
Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
255
Heart Sounds
1.7K
Heart sounds are generated by the turbulence in blood flow due to the closing of heart valves. These sounds are best perceived slightly away from the valves, where the blood flow disseminates the sound.
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
S1, also known as the "lub" sound, is caused by the closure of atrioventricular (A-V)...
1.7K


