Open Access
| Issue |
BIO Web Conf.
Volume 237, 2026
2026 8th International Conference on Biotechnology and Biomedicine (ICBB 2026)
|
|
|---|---|---|
| Article Number | 03023 | |
| Number of page(s) | 6 | |
| Section | Biomaterials, Medical Devices and Biomedical Engineering | |
| DOI | https://doi.org/10.1051/bioconf/202623703023 | |
| Published online | 10 June 2026 | |
- N.N. Sabrina, S. Novi, H. Bortfeld, R.B. Mitchell, A. Isaiah, Polysomnographic versus parent-reported predictors of executive function in children with sleep disordered breathing, Int. J. Pediatr. Otorhinolaryngol. 203, 112766 (2026) [Google Scholar]
- S.K. Chowdhury, Y. Zhang, Experimental analysis of collapsible tube dynamics in pulsatile flow conditions, J. Fluids Struct. 140, 104468 (2026) [Google Scholar]
- Q.Y. Li, H.P. Li, [Exploration and understanding of pathogenesis of obstructive sleep apnea], Zhonghua Jie He He Hu Xi Za Zhi 44, 864–866 (2021) [Google Scholar]
- B. Jiang, H. Wang, Y. Zhou, J. Wang, Deposition of coal dust particles in the human upper respiratory tract during unsteady breathing: Insights from CFD-DPM simulation and XGBoost-SHAP analysis, Powder Technol. 473, 122173 (2026) [Google Scholar]
- S. Shujaat, O. Jazil, H. Willems, A. Van Gerven, E. Shaheen, C. Politis, R. Jacobs, Automatic segmentation of the pharyngeal airway space with convolutional neural network, J. Dent. 111, 103705 (2021) [Google Scholar]
- J. Zhang, L. Ren, Enhance four-dimensional cone-beam computed tomography (4D-CBCT) from sparse view acquisitions using a novel deep learning model, Biomed. Signal Process. Control 119, 109935 (2026) [Google Scholar]
- A. Ohashi, M. Kataoka, M. Iima, S. Kanao, M. Honda, Y. Urushibata, M.D. Nickel, A.O. Kishimoto, R. Ota, M. Toi, K. Togashi, Amultiparametric approach to diagnosing breast lesions using diffusion-weighted imaging and ultrafast dynamic contrast-enhanced MRI, Magn. Reson. Imaging 71, 154–160 (2020) [Google Scholar]
- Y. Ying, Y. Zhao, X. Zhao, T. Gao, A. Li, X. Huang, G. Song, DIT-SAM: Enhancing segment anything model for automatic medical image segmentation via dual-interactive tuning, Biomed. Signal Process. Control 120, 110042 (2026) [Google Scholar]
- G. Wang, Y. Qu, Y. Li, A hybrid multi-scale/finite element method in arbitrary Lagrangian-Eulerian framework for predicting nonlinear structural-acoustic responses of a large-deformable beam in fluid, J. Sound Vib. 577, 118333 (2024) [Google Scholar]
- W. Ashraf, N. Jacobson, N. Popplewell, Z. Moussavi, Fluid-structure interaction modelling of the upper airway with and without obstructive sleep apnea: a review, Med. Biol. Eng. Comput. 60, 1827–1849 (2022) [Google Scholar]
- C.A. Chu, Y.J. Chen, K.V. Chang, W.T. Wu, L. Özçakar, Reliability of Sonoelastography Measurement of Tongue Muscles and Its Application on Obstructive Sleep Apnea, Front. Physiol. 12, 654667 (2021) [Google Scholar]
- M.R. Bonsignore, E. Mazzuca, P. Baliamonte et al., REM sleep obstructive sleep apnoea, Eur. Respir. Rev. 33, 230166 (2024) [Google Scholar]
- J.L. Stauffer, C.W. Zwillich, R.J. Cadieux et al., Pharyngeal size and resistance in obstructive sleep apnea, Am. Rev. Respir. Dis. 136, 623–627 (1987) [Google Scholar]
- S.H. Launois, T.R. Feroah, W.N. Campbell et al., Site of pharyngeal narrowing predicts outcome of surgery for obstructive sleep apnea, Am. Rev. Respir. Dis. 147, 182–189 (1993) [Google Scholar]
- R.C. Dedhia, E.R. Thuler, E.G. Seay et al., Pharyngeal manometry and upper airway collapse during drug-induced sleep endoscopy, JAMA Otolaryngol. Head Neck Surg. (2024) [Google Scholar]
- M. Moqri, M. Krawczyk, P.A. Cistulli, S. Narayanan, D. Dobrosielski, K. Kairaitis et al., Evaluation of human obstructive sleep apnea using computational fluid dynamics, Commun. Biol. 2, 443 (2019) [Google Scholar]
- S.A. Joosten, D.M. O’Driscoll, P.J. Berger, G.S. Hamilton, Variability of human upper airway collapsibility during sleep and the influence of body posture and sleep stage, J. Sleep Res. 20, 509–515 (2011) [Google Scholar]
- T. Fovet, F. Puel, D. Letourneur et al., Airway stability in sleep apnea: Assessing continuous positive airway pressure efficiency, Respir. Physiol. Neurobiol. (2024) [Google Scholar]
- T. Wakayama, M. Suzuki, T. Tanuma, Effect of Nasal Obstruction on Continuous Positive Airway Pressure Treatment: Computational Fluid Dynamics Analyses, PLoS ONE 11, e0150951 (2016) [Google Scholar]
- M. Zhao, T. Barber, P. Cistulli et al., Computational fluid dynamics for the assessment of upper airway response to oral appliance treatment in obstructive sleep apnea, J. Biomech. 46, 142–150 (2013) [Google Scholar]
- L. Zhu, H. Liu, Z. Fu et al., Computational fluid dynamics analysis of H-uvulopalatopharyngoplasty in obstructive sleep apnea syndrome, Am. J. Otolaryngol. 40, 197–204 (2019) [Google Scholar]
- M.D. Johnson, Y.M. Dweiri, J. Cornelius et al., Model-based analysis of implanted hypoglossal nerve stimulation for the treatment of obstructive sleep apnea, Sleep 44, S11–S19 (2021) [Google Scholar]
- R.J. Soose, M.B. Gillespie, B.T. Woodson et al., Targeted Hypoglossal Nerve Stimulation for Patients With Obstructive Sleep Apnea: A Randomized Clinical Trial, JAMA Otolaryngol. Head Neck Surg. (2023) [Google Scholar]
- B. Song, Y. Li, J. Sun, Y. Qi, P. Li, Y. Li, Z. Gu, Computational fluid dynamics simulation of changes in the morphology and airflow dynamics of the upper airways in OSAHS patients after treatment with oral appliances, PLoS ONE 14, e0219642 (2019) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

