Diagnosis of approximal caries in children with convolutional neural networks based detection algorithms on radiographs: A pilot study.

Journal: Acta odontologica Scandinavica
PMID: 39761112

Abstract

OBJECTIVES: Approximal caries diagnosis in children is difficult, and artificial intelligence-based research in pediatric dentistry is scarce. To create a convolutional neural network (CNN)-based diagnostic system for the prompt and efficient identification of approximal caries in pediatric patients aged 5-12 years.

Authors

  • Zeynep Seyda Yavsan
    Department of Pediatric Dentistry, Tekirdag Namik Kemal University, Tekirdag, Turkey. ylmzynpsyda@gmail.com.
  • Hediye Orhan
    Department of Computer Engineering, Necmettin Erbakan University, Konya, Turkey.
  • Enes Efe
    Department of Electrical and Electronics Engineering, Hitit University, Corum 19030, Turkey.
  • Emrehan Yavsan
    Department of Electronics and Automation, Tekirdag Namik Kemal University, Tekirdag 59030, Turkey.

Keywords

External Resources

Popular Topics
Recent Journals