Copyright
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Sep 28, 2025; 17(9): 109116
Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.109116
Published online Sep 28, 2025. doi: 10.4329/wjr.v17.i9.109116
Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review
Li Ding, School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, Anhui Province, China
Jian-Xin Peng, Yu-Jun Song, School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510640, Guangdong Province, China
Author contributions: Peng JX, Ding L designed the research study; Song YJ, Ding L performed the literature review and conducted the data analysis; Ding L wrote the manuscript.
Supported by the National Natural Science Foundation of China, No. 11974121; and Talent Research Fund of Hefei University, No. 24RC08.
Conflict-of-interest statement: The authors declare no conflicts of interest related to this work.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Jian-Xin Peng, Professor, School of Physics and Optoelectronics, South China University of Technology, No. 381 Wushan Road, Tianhe District, Guangzhou 510640, Guangdong Province, China. phjxpeng@163.com
Received: April 30, 2025
Revised: May 31, 2025
Accepted: August 13, 2025
Published online: September 28, 2025
Processing time: 150 Days and 4.4 Hours
Revised: May 31, 2025
Accepted: August 13, 2025
Published online: September 28, 2025
Processing time: 150 Days and 4.4 Hours
Core Tip
Core Tip: This systematic review summarizes recent advances in the use of image-based deep learning models for snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS). The review highlights the role of time–frequency representations and deep learning architectures in classifying snoring types and estimating severity of OSAHS. The work also identifies current challenges in data standardization, model interpretability, and clinical integration, providing direction for future research.