| For: | 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; 17(9): 109116 [PMID: 41025059 DOI: 10.4329/wjr.v17.i9.109116] |
|---|---|
| URL: | https://www.wjgnet.com/1949-8470/full/v17/i9/109116.htm |
| Number | Citing Articles |
| 1 |
Blessing C. Uzo, Collins N. Udanor, Ponsak S. Bande, Adaora A. Obayi, Stanley E. Abhadiomhen, Nnamdi Ugwuoke, George Obaido, Blessing Ogbuokiri. Snoring-based audio analysis for obstructive sleep apnea detection: A scoping review of machine learning models and explainability approaches. Machine Learning with Applications 2026; 26 doi: 10.1016/j.mlwa.2026.101029
|
| 2 |
Melike Aygün Çakıroğlu, Emel Kızılkaya Aydoğan, Ömer Faruk Bolattürk, Serhat Aydoğan, Sevda İsmailoğulları, Yılmaz Delice. Non-contact acoustic screening for sleep apnea: a subject-aware deep learning approach. Sleep and Breathing 2026; 30(1) doi: 10.1007/s11325-026-03594-2
|
| 3 |
Khaled Trabelsi, Achraf Ammar, Seithikurippu R. Pandi-Perumal, Haitham Jahrami. Regulatory, Legal, and Ethical Challenges in Artificial Intelligence-powered Sleep Medicine. Sleep Medicine Clinics 2026; doi: 10.1016/j.jsmc.2026.08.021
|