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

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