Performance of artificial intelligence on cervical vertebral maturation assessment: a systematic review and meta-analysis.

Journal: BMC oral health
PMID: 39910512

Abstract

BACKGROUND: Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such as cervical vertebral maturation (CVM) staging have limitations due to observer variability and complexity. AI has the potential to automate CVM assessment, enhancing reliability and user-friendliness. This systematic review and meta-analysis aimed to evaluate the overall performance of artificial intelligence (AI) models in assessing cervical vertebrae maturation (CVM) in radiographs, when compared to clinicians.

Authors

  • Termeh Sarrafan Sadeghi
    Dentofacial Deformities Research center, Research Institute of Dental sciences, Shahid Beheshti, University of Medical Sciences, Tehran, Iran.
  • Seyed AmirHossein Ourang
    Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Fatemeh Sohrabniya
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Soroush Sadr
    Department of Endodontics, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Parnian Shobeiri
    School of Medicine, Tehran University of Medical Science, Tehran, Iran.
  • 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.

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