PDF(Pubmed)
Abstract：
BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.
OBJECTIVE: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.
METHODS: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values.
RESULTS: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.
CONCLUSIONS: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally.
UNASSIGNED: PRR1-10.2196/54388.
OBJECTIVE: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.
METHODS: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values.
RESULTS: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.
CONCLUSIONS: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally.
UNASSIGNED: PRR1-10.2196/54388.
摘要：
背景：呼吸系统疾病，包括活动性结核病（TB），哮喘,和慢性阻塞性肺疾病（COPD），构成重大的全球卫生挑战，需要及时准确的诊断以进行有效的治疗和管理。
目的：本研究旨在开发和评估一种无创的用户友好型人工智能（AI）驱动的咳嗽音频分类器，以检测坦桑尼亚农村的这些呼吸状况。
方法：这是一项非实验性的横断面研究，主要目的是收集和分析活动性结核病患者的咳嗽声音，哮喘,和COPD在门诊诊所生成和评估非侵入性咳嗽音频分类器。专门的咳嗽录音设备，设计为非侵入性和用户友好，将有助于收集来自Shinyanga地区20个医疗机构门诊的患者的各种咳嗽声音样本。收集的咳嗽声音数据将经过严格的分析，使用先进的AI信号处理和机器学习技术。通过比较与TB相关的声学特征和模式，哮喘,COPD,将生成能够自动辨别疾病的稳健算法,从而促进基于智能手机的咳嗽声音分类器的开发.分类器将根据计算的参考标准进行评估，包括临床评估，痰涂片,GeneXpert,胸部X光,文化和敏感性，肺活量测定和峰值呼气流量，以及灵敏度和预测值。
结果：这项研究代表了提高门诊诊所诊断能力的重要一步。有可能彻底改变呼吸疾病诊断领域。研究四个阶段的结果将作为相关图像支持的描述呈现，tables,和数字。这项研究的预期结果是创建一个可靠的，非侵入性诊断咳嗽分类器，使医疗保健专业人员和患者自己能够根据咳嗽声音模式识别和区分这些呼吸道疾病。
结论：咳嗽声音分类器使用先进的技术来早期检测和管理呼吸系统疾病，为传统诊断提供一种侵入性更低、更有效的替代方案。这项技术有望减轻公共卫生负担，改善患者预后，加强资源不足地区的医疗保健服务，可能改变全球呼吸道疾病管理。
■PRR1-10.2196/54388。
目的：本研究旨在开发和评估一种无创的用户友好型人工智能（AI）驱动的咳嗽音频分类器，以检测坦桑尼亚农村的这些呼吸状况。
方法：这是一项非实验性的横断面研究，主要目的是收集和分析活动性结核病患者的咳嗽声音，哮喘,和COPD在门诊诊所生成和评估非侵入性咳嗽音频分类器。专门的咳嗽录音设备，设计为非侵入性和用户友好，将有助于收集来自Shinyanga地区20个医疗机构门诊的患者的各种咳嗽声音样本。收集的咳嗽声音数据将经过严格的分析，使用先进的AI信号处理和机器学习技术。通过比较与TB相关的声学特征和模式，哮喘,COPD,将生成能够自动辨别疾病的稳健算法,从而促进基于智能手机的咳嗽声音分类器的开发.分类器将根据计算的参考标准进行评估，包括临床评估，痰涂片,GeneXpert,胸部X光,文化和敏感性，肺活量测定和峰值呼气流量，以及灵敏度和预测值。
结果：这项研究代表了提高门诊诊所诊断能力的重要一步。有可能彻底改变呼吸疾病诊断领域。研究四个阶段的结果将作为相关图像支持的描述呈现，tables,和数字。这项研究的预期结果是创建一个可靠的，非侵入性诊断咳嗽分类器，使医疗保健专业人员和患者自己能够根据咳嗽声音模式识别和区分这些呼吸道疾病。
结论：咳嗽声音分类器使用先进的技术来早期检测和管理呼吸系统疾病，为传统诊断提供一种侵入性更低、更有效的替代方案。这项技术有望减轻公共卫生负担，改善患者预后，加强资源不足地区的医疗保健服务，可能改变全球呼吸道疾病管理。
■PRR1-10.2196/54388。
