Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review
Challenges such as dataset variability, generalizability, model interpretability, and deployment feasibility are also discussed. Future directions highlight the importance of explainable artificial intelligence and domain-adaptive learning for clinically viable OSAHS diagnostic tools.
Authors
- Jianxin Peng (ORCID: https://orcid.org/0000-0002-3469-1781)
- Yujun Song (ORCID: https://orcid.org/0000-0003-2474-084X)
- Li Ding (ORCID: https://orcid.org/0000-0002-2393-1188)
Institutions
- Guangdong University of Technology (CN)
- Hefei University of Technology (CN)
- Hefei University (CN)
- South China University of Technology (CN)
Publication Details
- Journal
- World Journal of Radiology
- Published
- 2025-09-26
- DOI
- https://doi.org/10.4329/wjr.v17.i9.109116
- Citations
- 1
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 1.16