Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review.

Ding L, Peng JX, Song YJ

DOI: 10.4329/wjr.v17.i9.109116
Published: 2025 Sep 28
Container: World journal of radiology
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.4329/wjr.v17.i9.109116,
  title = {Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review.},
  author = {Ding L and Peng JX and Song YJ},
  year = {2025},
  journal = {World journal of radiology},
  doi = {10.4329/wjr.v17.i9.109116},
  url = {https://doi.org/10.4329/wjr.v17.i9.109116}
}

RIS

TY  - JOUR
TI  - Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review.
AU  - Ding L
AU  - Peng JX
AU  - Song YJ
PY  - 2025
JO  - World journal of radiology
DO  - 10.4329/wjr.v17.i9.109116
UR  - https://doi.org/10.4329/wjr.v17.i9.109116
ER  -

APA

L, D., JX, P., & YJ, S. (2025). Deep learning approaches for image-based snoring sound analysis in the diagnosis of obstructive sleep apnea-hypopnea syndrome: A systematic review.. World journal of radiology. https://doi.org/10.4329/wjr.v17.i9.109116

Source records

pubmed · retrieved 2026-09-26T03:55:15.238Z