首页|Aga Khan University Reports Findings in Chronic Obstructive Pulmonary Disease (L everaging AI and Machine Learning to Develop and Evaluate a Contextualized User- Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a ...)
Aga Khan University Reports Findings in Chronic Obstructive Pulmonary Disease (L everaging AI and Machine Learning to Develop and Evaluate a Contextualized User- Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Lung Diseases and Cond itions - Chronic Obstructive Pulmonary Disease is the subject of a report. Accor ding to news reporting from Dar Es Salaam, Tanzania, by NewsRx journalists, rese arch stated, “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 tre atment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for d etecting these respiratory conditions in rural Tanzania.” The news correspondents obtained a quote from the research from Aga Khan Univers ity, “This is a nonexperimental cross-sectional research with the primary object ive of collection and analysis of cough sounds from patients with active TB, ast hma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be non intrusive and user-friendly, will facilitate the collection of diverse cough sou nd samples from patients attending outpatient clinics in 20 health care faciliti es 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 generat ed facilitating the development of a smartphone-based cough sound classifier. Th e classifier will be evaluated against the calculated reference standards includ ing clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sens itivity, spirometry and peak expiratory flow, and sensitivity and predictive val ues. This research represents a vital step toward enhancing the diagnostic capab ilities 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 fig ures. The anticipated outcome of this research is the creation of a reliable, no ninvasive diagnostic cough classifier that empowers health care professionals an d patients themselves to identify and differentiate these respiratory diseases b ased on cough sound patterns. Cough sound classifiers use advanced technology fo r early detection and management of respiratory conditions, offering a less inva sive and more efficient alternative to traditional diagnostics.”
Dar Es SalaamTanzaniaAfricaAsthmaBronchial Diseases and ConditionsChronic Obstructive Pulmonary DiseaseCybor gsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineImmun e System Diseases and ConditionsInfectious DiseaseLung Diseases and Conditio nsMachine LearningMycobacterium InfectionsObstructive Lung Diseases and Co nditionsRespiratory HypersensitivityRespiratory Tract Diseases and Condition sTechnologyTuberculosis
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