Deep Learning for Automated Prediction of Sphenoid Sinus Pneumatization in Computed Tomography
The proposed CNN model demonstrates very good diagnostic accuracy in identifying various sphenoid sinus pneumatization patterns, particularly excelling in type IV, which is crucial for endoscopic sinus surgery due to its higher risk of surgical complications. By assisting radiologists and surgeons, this model enhances the safety of transsphenoidal surgery, highlighting its value, novelty, and applicability in clinical settings.
Authors
- Ali Alamer (ORCID: https://orcid.org/0000-0002-3121-9537)
- Khaled Alhassoon (ORCID: https://orcid.org/0000-0002-1780-2857)
- Fahd Alsaleem (ORCID: https://orcid.org/0000-0002-8010-6678)
- Omar Salim (ORCID: https://orcid.org/0000-0002-7487-2595)
- Fahad N. Alsunaydih (ORCID: https://orcid.org/0000-0001-8615-151X)
- Fawaz Alharbi (ORCID: https://orcid.org/0000-0002-0897-9415)
- Afnan S. Almuqbil
Institutions
- Qassim University (SA)
- Monash University (AU)
Publication Details
- Journal
- Current Medical Imaging Formerly Current Medical Imaging Reviews
- Published
- 2025-05-26
- DOI
- https://doi.org/10.2174/0115734056363158250429101521
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 0.00