An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism
JOURNAL OF ADVANCED ORAL RESEARCH, cilt.12, sa.2, ss.206-213, 2021 (ESCI, Scopus)
- 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.