Deep learning and radiomics-based vascular calcification characterization in dental cone beam computed tomography as a predictive tool for cardiovascular disease: a proof-of-concept study.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
PMID: 39827035

Abstract

OBJECTIVES: This study evaluated an automated deep learning method for detecting calcifications in the extracranial and intracranial carotid arteries and vertebral arteries in cone beam computed tomography (CBCT) scans. Additionally, a model utilizing CBCT-derived radiomics imaging biomarkers was evaluated to predict the cardiovascular diseases (CVD) of stroke and heart attack.

Authors

  • Mina Mahdian
    Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Amr A Ahmed
    Department of Prosthodontics and Digital Technology, School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Moinak Bhattacharya
    Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Prateek Prasanna
    Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States.

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