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.

Journal: JMIR formative research
PMID: 39226096

Abstract

BACKGROUND: Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied.

Authors

  • Shishir Shetty
    National Institute for Health Research Birmingham Liver Biomedical Research Unit and Centre for Liver and Gastrointestinal Research, Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom.
  • Auwalu Saleh Mubarak
    Operational Research Centre in Healthcare, Near East University, TRNC Mersin 10, Nicosia, 99138, Turkey.
  • Leena R David
    Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates.
  • Mhd Omar Al Jouhari
    Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
  • Wael Talaat
  • Natheer Al-Rawi
    Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, United Arab Emirates.
  • Sausan AlKawas
    Department of Oral and Craniofacial Health Sciences, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
  • Sunaina Shetty
    Department of Restorative and Preventive Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, UAE.
  • Dilber Uzun Ozsahin
    Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates.

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