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, vol.12, no.2, pp.206-213, 2021 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 12 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.1177/23202068211005611
  • Journal Name: JOURNAL OF ADVANCED ORAL RESEARCH
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Page Numbers: pp.206-213
  • Keywords: Artificial intelligence, Deep learning, Ultrasonography, Masseter muscle, MAGNETIC-RESONANCE IMAGES, SONOGRAPHIC ELASTOGRAPHY, LEARNING CLASSIFICATION, ULTRASOUND ELASTOGRAPHY, MORPHOLOGY, SLEEP
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

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.