Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives DOI
Basheer Abdullah Marzoog, F. Yu. Kopylov

World Journal of Cardiology, Journal Year: 2025, Volume and Issue: 17(4)

Published: April 21, 2025

Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use, particularly ischemic heart disease (IHD). However, current research on volatilome (exhaled composition) in remains underexplored and lacks sufficient evidence to confirm its validity. Key challenges hindering application diagnosing IHD include scarcity studies (only three published papers date), substantial methodological bias two these studies, absence standardized protocols implementation. Additionally, inconsistencies methodologies-such sample collection, analytical techniques, machine learning (ML) approaches, result interpretation-vary widely across further complicating their reproducibility comparability. To address gaps, there is an urgent need establish unified guidelines that define best practices data analysis, ML integration, biomarker annotation. Until are systematically resolved, widespread adoption reliable diagnostic distant goal rather than imminent reality.

Language: Английский

Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives DOI
Basheer Abdullah Marzoog, F. Yu. Kopylov

World Journal of Cardiology, Journal Year: 2025, Volume and Issue: 17(4)

Published: April 21, 2025

Integrating exhaled breath analysis into the diagnosis of cardiovascular diseases holds significant promise as a valuable tool for future clinical use, particularly ischemic heart disease (IHD). However, current research on volatilome (exhaled composition) in remains underexplored and lacks sufficient evidence to confirm its validity. Key challenges hindering application diagnosing IHD include scarcity studies (only three published papers date), substantial methodological bias two these studies, absence standardized protocols implementation. Additionally, inconsistencies methodologies-such sample collection, analytical techniques, machine learning (ML) approaches, result interpretation-vary widely across further complicating their reproducibility comparability. To address gaps, there is an urgent need establish unified guidelines that define best practices data analysis, ML integration, biomarker annotation. Until are systematically resolved, widespread adoption reliable diagnostic distant goal rather than imminent reality.

Language: Английский

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