Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives

doi:10.4330/wjc.v17.i4.106593

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World Journal of Cardiology

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1949-8462

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Letter to the Editor

World J Cardiol. Apr 26, 2025; 17(4): 106593
Published online Apr 26, 2025. doi: 10.4330/wjc.v17.i4.106593

Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives

Basheer Abdualah Marzoog, Philipp Kopylov

Basheer Abdualah Marzoog, Philipp Kopylov, World-Class Research Center (Digital Biodesign and Personalized Healthcare), I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991 Moscow, Russia

Author contributions: Marzoog BA wrote the manuscript, conducted research, collected and analyzed data, interpreted the results, and revised the final version of the manuscript; Kopylov P revised the final version of the manuscript and contributed to the conceptualization and development; all authors have read and approved the final version of the manuscript to be published.

Supported by The government assignment, No. 1023022600020-6; The Ministry of Science and Higher Education of the Russian Federation Within The Framework of State Support for The Creation and Development of World-Class Research Center “Digital Biodesign and Personalized Healthcare,” No. 075-15-2022-304; and RSF grant, No. 24-15-00549.

Conflict-of-interest statement: No competing interests regarding the publication.

Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Basheer Abdualah Marzoog, MD, Digital Biodesign and Personalized Healthcare, Sechenov University, 8-2 Trubetskaya Street, Moscow 119991, Moskva, Russia. marzug@mail.ru

Received: March 3, 2025
Revised: March 14, 2025
Accepted: April 1, 2025
Published online: April 26, 2025
Processing time: 50 Days and 20.1 Hours

Core Tip

Core Tip: Exhaled breath analysis offers a non-invasive, cost-effective alternative to traditional ischemic heart disease diagnostics, with superior accuracy (84% vs 60%–70% for stress electrocardiography). To enhance reliability, standardized protocols for breath collection and the integration of machine learning are essential. Collaborative efforts among clinicians, chemists, and data scientists are key to unlocking its full clinical potential.