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

Formative Evaluation of Digital Health Interventions (2816) Imaging Informatics (235) Artificial Intelligence (1732) ENT (Ear, Nose, Throat) (32) Medical imaging (62)

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 (2) 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