Streamlining heart failure patient care with machine learning of thoracic cavity sound data

doi:10.4330/wjc.v17.i9.109992

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Sep 26, 2025 (publication date) through Sep 17, 2025

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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

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World J Cardiol. Sep 26, 2025; 17(9): 109992
Published online Sep 26, 2025. doi: 10.4330/wjc.v17.i9.109992

Streamlining heart failure patient care with machine learning of thoracic cavity sound data

Rony Marethianto Santoso, Wilbert Huang, Ser Wee, Bambang Budi Siswanto, Amiliana Mardiani Soesanto, Wisnu Jatmiko, Aria Kekalih

Rony Marethianto Santoso, Faculty of Medicine, University Indonesia, Jakarta 40416, Indonesia

Wilbert Huang, Faculty of Medicine, University of Padjadjaran, Bandung 40416, West Java, Indonesia

Ser Wee, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore

Bambang Budi Siswanto, Amiliana Mardiani Soesanto, Department of Cardiovascular Medicine, University Indonesia, Jakarta 40416, Indonesia

Wisnu Jatmiko, Faculty of Computer Science, University Indonesia, Jakarta 40416, Indonesia

Aria Kekalih, Department of Community Medicine, University Indonesia, Jakarta 40416, Indonesia

Co-first authors: Rony Marethianto Santoso and Wilbert Huang.

Author contributions: Huang W, Santoso RM contributed to conceptualization, data curation, writing-original draft, review and editing; Siswanto BB, Soesanto AM, Jatmiko W, Kekalih A contributed to supervision; Wee S contributed to validation.

Conflict-of-interest statement: The authors report no relevant conflicts of interest for this article.

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: Wilbert Huang, MD, Faculty of Medicine, University of Padjadjaran, Universitas Padjadjaran, West Java, Bandung 40416, Indonesia. wilberthuang67@gmail.com

Received: May 28, 2025
Revised: June 6, 2025
Accepted: August 5, 2025
Published online: September 26, 2025
Processing time: 113 Days and 5.2 Hours

Core Tip

Core Tip: Machine learning could aid in improving healthcare in heart failure (HF) patients. HF is a rapidly progressing chronic disease; therefore, improving care by enhancing diagnostic and prognostic modalities is essential. Machine learning of thoracic cavity sounds in HF patients will help define the characteristics of HF patients.