Artificial Intelligence in Respiratory Health: A Review of AI-Driven Analysis of Oral and Nasal Breathing Sounds for Pulmonary Assessment DOI Open Access
Shiva Shokouhmand, Smriti Bhatt, Miad Faezipour

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(10), P. 1994 - 1994

Published: May 14, 2025

Continuous monitoring of pulmonary function is crucial for effective respiratory disease management. The COVID-19 pandemic has also underscored the need accessible and convenient diagnostic tools health assessment. While traditional lung sound auscultation been primary method evaluating function, emerging research highlights potential nasal oral breathing sounds. These sounds, shaped by upper airway, serve as valuable non-invasive biomarkers detection. Recent advancements in artificial intelligence (AI) have significantly enhanced analysis enabling automated feature extraction pattern recognition from spectral temporal characteristics or even raw acoustic signals. AI-driven models demonstrated promising accuracy detecting conditions, paving way real-time, smartphone-based monitoring. This review examines AI-enhanced analysis, discussing methodologies, available datasets, future directions toward scalable solutions.

Language: Английский

Leveraging CQT-VMD and pre-trained AlexNet architecture for accurate pulmonary disease classification from lung sound signals DOI
Zakaria Neili, Kenneth Sundaraj

Applied Intelligence, Journal Year: 2025, Volume and Issue: 55(6)

Published: March 20, 2025

Language: Английский

Citations

0
Artificial Intelligence in Respiratory Health: A Review of AI-Driven Analysis of Oral and Nasal Breathing Sounds for Pulmonary Assessment DOI Open Access
Shiva Shokouhmand, Smriti Bhatt, Miad Faezipour

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(10), P. 1994 - 1994

Published: May 14, 2025

Continuous monitoring of pulmonary function is crucial for effective respiratory disease management. The COVID-19 pandemic has also underscored the need accessible and convenient diagnostic tools health assessment. While traditional lung sound auscultation been primary method evaluating function, emerging research highlights potential nasal oral breathing sounds. These sounds, shaped by upper airway, serve as valuable non-invasive biomarkers detection. Recent advancements in artificial intelligence (AI) have significantly enhanced analysis enabling automated feature extraction pattern recognition from spectral temporal characteristics or even raw acoustic signals. AI-driven models demonstrated promising accuracy detecting conditions, paving way real-time, smartphone-based monitoring. This review examines AI-enhanced analysis, discussing methodologies, available datasets, future directions toward scalable solutions.

Language: Английский

Citations

0