Detection of maxillary sinus pathologies using deep learning algorithms
This study successfully developed an AI-driven approach for segmenting maxillary sinus pathologies in CBCT images. The findings highlight the potential of this method for rapid and accurate clinical assessment of maxillary sinus conditions using CBCT imaging.
Authors
- Kaan Orhan (ORCID: https://orcid.org/0000-0001-6768-0176)
- Nurullah Akkaya (ORCID: https://orcid.org/0000-0002-4070-7444)
- Seçil Aksoy (ORCID: https://orcid.org/0000-0002-6400-4911)
- Aida Kurbanova (ORCID: https://orcid.org/0000-0002-8282-792X)
- Ceren Aktuna Belgın (ORCID: https://orcid.org/0000-0001-7780-3395)
Institutions
- Semmelweis University (HU)
- Ankara University (TR)
- Turkish Society of Hematology (TR)
- Mustafa Kemal University (TR)
- Near East University (CY)
Publication Details
- Journal
- European Archives of Oto-Rhino-Laryngology
- Published
- 2025-05-20
- DOI
- https://doi.org/10.1007/s00405-025-09451-4
- Citations
- 1
- Primary Topic
- Sinusitis and nasal conditions
- Type
- article
- Field-Weighted Citation Impact
- 1.85
Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu