Publication Name	World Journal of Cardiology
Manuscript ID	109992
Country	Indonesia

Received	2025-05-28 09:12
Peer-Review Started	2025-05-28 09:12
To Make the First Decision
Return for Revision	2025-06-06 03:39
Revised	2025-06-06 12:12
Second Decision	2025-08-05 02:48
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-08-05 11:53
Articles in Press	2025-08-05 11:53
Publication Fee Transferred
Edit the Manuscript by Language Editor	2025-08-11 05:31
Typeset the Manuscript	2025-09-03 08:17
Publish the Manuscript Online	2025-09-17 06:30

ISSN	1949-8462 (online)
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/
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Category	Cardiac & Cardiovascular Systems
Manuscript Type	Minireviews
Article Title	Streamlining heart failure patient care with machine learning of thoracic cavity sound data
Manuscript Source	Invited Manuscript
All Author List	Rony Marethianto Santoso, Wilbert Huang, Ser Wee, Bambang Budi Siswanto, Amiliana Mardiani Soesanto, Wisnu Jatmiko and Aria Kekalih
Funding Agency and Grant Number
Corresponding Author	Wilbert Huang, MD, Faculty of Medicine, University of Padjadjaran, Universitas Padjadjaran, Bandung, West Java, Indonesia, Bandung 40416, West Java, Indonesia. wilberthuang67@gmail.com
Key Words	Machine learning; Heart failure; Sound data; Artificial intelligence; Deep learning
Core Tip	Machine learning could aid in improving healthcare in heart failure (HF) patients. HF is a chronic disease that is rapidly progressing hence, improving care by enhancing diagnostic and prognostic modalities are key important pillar. Machine learning of thoracic cavity sound in HF patient will be able to determine the characteristics of HF patients.
Publish Date	2025-09-17 06:30
Citation	<p>Santoso RM, Huang W, Wee S, Siswanto BB, Soesanto AM, Jatmiko W, Kekalih A. Streamlining heart failure patient care with machine learning of thoracic cavity sound data. <i>World J Cardiol</i> 2025; 17(9): 109992</p>
URL	https://www.wjgnet.com/1949-8462/full/v17/i9/109992.htm
DOI	https://dx.doi.org/10.4330/wjc.v17.i9.109992

Full Article (PDF)	WJC-17-109992-with-cover.pdf
Manuscript File	109992_Auto_Edited_011643.docx
Answering Reviewers	109992-answering-reviewers.pdf
Audio Core Tip	109992-audio.mp4
Conflict-of-Interest Disclosure Form	109992-conflict-of-interest-statement.pdf
Copyright License Agreement	109992-copyright-assignment.pdf
Non-Native Speakers of English Editing Certificate	109992-non-native-speakers.pdf
Peer-review Report	109992-peer-reviews.pdf
Scientific Misconduct Check	109992-scientific-misconduct-check.png
Scientific Editor Work List	109992-scientific-editor-work-list.pdf
CrossCheck Report	109992-crosscheck-report.pdf