A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.

Journal: BMC oral health
PMID: 39180070

Abstract

OBJECTIVES: Canine-induced root resorption (CIRR) is caused by impacted canines and CBCT images have shown to be more accurate in diagnosing CIRR than panoramic and periapical radiographs with the reported AUCs being 0.95, 0.49, and 0.57, respectively. The aim of this study was to use deep learning to automatically evaluate the diagnosis of CIRR in maxillary incisors using CBCT images.

Authors

  • Zeynab Pirayesh
    Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.
  • Saeed Reza Motamedian
    Department of Orthodontics, School of Dentistry, & Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: drmotamedian@gmail.com.
  • Sepehr Amini Afshar
    Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
  • Reza Abbasi
    Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
  • Mohammad Hossein Rohban
    Computer Engineering Department, Sharif University of Technology, Tehran, Iran.
  • Mina Mahdian
    Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Mitra Ghazizadeh Ahsaie
    Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: mitraghazizadeh@sbmu.ac.ir.
  • Mina Iranparvar Alamdari
    Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran. mina.iranparvar@gmail.com.

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