This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3229) Imaging Informatics (302) Artificial Intelligence (2129) ENT (Ear, Nose, Throat) (36) Medical imaging (110)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57335, first published .
The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study

Authors of this article:

Shishir Shetty1 Author Orcid Image ;   Auwalu Saleh Mubarak2 Author Orcid Image ;   Leena R David3 Author Orcid Image ;   Mhd Omar Al Jouhari1 Author Orcid Image ;   Wael Talaat1 Author Orcid Image ;   Natheer Al-Rawi1 Author Orcid Image ;   Sausan AlKawas1 Author Orcid Image ;   Sunaina Shetty4 Author Orcid Image ;   Dilber Uzun Ozsahin3 Author Orcid Image
Article Authors Cited by (9) Tweetations Metrics

Journals

  1. Zhang S, Zhu Z, Yu Z, Sun H, Sun Y, Huang H, Xu L, Wan J. Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e66622 View
  2. Shetty S, Talaat W, Al-Rawi N, Al Kawas S, Sadek M, Elayyan M, Gaballah K, Narasimhan S, Ozsahin I, Ozsahin D, David L. Accuracy of deep learning models in the detection of accessory ostium in coronal cone beam computed tomographic images. Scientific Reports 2025;15(1) View
  3. Chen J, Fan H, Wang Y, Zhu Y, Zhou R, Kang H, Jiang H. Construction and evaluation of a nomogram prediction model for the risk of epistaxis following nasotracheal intubation: a single-center retrospective cohort study. BMC Anesthesiology 2025;25(1) View
  4. Naga Srinivasu P, Aruna Kumari G, Jaya Kumari D, Barsocchi P, Kumar Bhoi A. SegAN for Recognition of Caries From 2D-Panoramic X-Ray Images. IEEE Access 2025;13:100419 View
  5. Chen J. Convolutional neural network for maxillary sinus segmentation based on the U-Net architecture at different planes in the Chinese population: a semantic segmentation study. BMC Oral Health 2025;25(1) View
  6. Wang Q, Li S, Sun H, Cui S, Song W. A Multimodal Classification Method for Nasal Obstruction Severity Based on Computed Tomography and Nasal Resistance. Annals of the New York Academy of Sciences 2025;1553(1):541 View
  7. Zhai Q, Cui M, Fu Y, Huang X, Wang Z, Wu Q, Cong N, Liu C. Automated assessment of nasal septum deviation using cone-beam computed tomography images based on artificial intelligence: Development and multicenter validation. American Journal of Orthodontics and Dentofacial Orthopedics 2025 View
  8. Chaudhary M, Dahan N. Artificial Intelligence-Enhanced Cone-Beam Computed Tomography for the Diagnostic Evaluation of Chronic Rhinosinusitis: A Systematic Review. Cureus 2025 View
  9. Alamer A, Salim O, Alharbi F, Alsaleem F, Almuqbil A, Alhassoon K, Alsunaydih F. Deep Learning for Automated Prediction of Sphenoid Sinus Pneumatization in Computed Tomography. Current Medical Imaging Formerly Current Medical Imaging Reviews 2025;21 View