Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
PMID: 37770329

Abstract

OBJECTIVE: We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images.

Authors

  • Shahd A Alajaji
    Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland Baltimore, MD, USA; Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA; Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
  • Rula Amarin
    Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, MD, USA.
  • Radi Masri
    Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland, Baltimore, MD, USA.
  • Tiffany Tavares
    School of Dentistry, University of Missouri-Kansas City, Kansas City, MO, USA.
  • Vandana Kumar
    Department of Oncology and Diagnostic Sciences, School of Dentistry, University of Maryland Baltimore, MD, USA.
  • Jeffery B Price
    Department of Oncology and Diagnostic Sciences, University of Maryland, School of Dentistry, Baltimore, Maryland, USA.
  • Ahmed S Sultan
    School of Dentistry, University of Maryland, Baltimore, MD, USA.

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