Publication Name	World Journal of Radiology
Manuscript ID	109116
Country	China

Category	Engineering, Biomedical
Manuscript Type	Systematic Reviews
Article Title	Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review
Manuscript Source	Invited Manuscript
All Author List	Li Ding, Jian-Xin Peng and Yu-Jun Song
Funding Agency and Grant Number	Funding Agency Grant Number National Natural Science Foundation of China 11974121 Talent Research Fund of Hefei University 24RC08
Corresponding Author	Jian-Xin Peng, Professor, School of Physics and Optoelectronics, South China University of Technology, wuhan, Guangzhou 510640, Guangdong Province, China. phjxpeng@163.com
Key Words	Obstructive sleep apnea hypopnea syndrome; Snoring sounds; Image; Neural network; Systematic review
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.
Citation	Ding L, Peng JX, Song YJ. Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review. World J Radiol 2025; In press

Received	2025-04-30 07:53
Peer-Review Started	2025-04-30 07:53
To Make the First Decision
Return for Revision	2025-05-17 08:50
Revised	2025-05-31 02:17
Second Decision	2025-08-13 05:20
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-08-13 09:37
Articles in Press	2025-08-13 09:37
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1949-8470 (online)
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