An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Orhan K., Yazıcı G., Kolsuz M. E., Kafa N., Bayrakdar İ. Ş., Çelik Ö.

JOURNAL OF ADVANCED ORAL RESEARCH, cilt.12, sa.2, ss.206-213, 2021 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 2
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1177/23202068211005611
  • Dergi Adı: JOURNAL OF ADVANCED ORAL RESEARCH
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
  • Sayfa Sayıları: ss.206-213
  • Anahtar Kelimeler: Artificial intelligence, Deep learning, Ultrasonography, Masseter muscle, MAGNETIC-RESONANCE IMAGES, SONOGRAPHIC ELASTOGRAPHY, LEARNING CLASSIFICATION, ULTRASOUND ELASTOGRAPHY, MORPHOLOGY, SLEEP
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images.