Assessment of deep learning technique for fully automated mandibular segmentation.

Journal: American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics

PMID: 39863342

Abstract

INTRODUCTION: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.

Authors

Ebru Yurdakurban

Department of Orthodontics, Gulhane Faculty of Dental Medicine, University of Health Sciences, Ankara, Türkiye.
Yağızalp Süküt

Department of Orthodontics, Gülhane Faculty of Dentistry, University of Health Sciences, Ankara 06010, Turkey. Electronic address: yagizalpsukut@gmail.com.
Gökhan Serhat Duran

Department of Orthodontics, Gulhane Faculty of Dental Medicine, University of Health Sciences, Ankara, Turkey.

Keywords

Cone-Beam Computed Tomography Deep Learning Female Humans Image Processing, Computer-Assisted Male Mandible Neural Networks, Computer

External Resources

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Assessment of deep learning technique for fully automated mandibular segmentation.

Abstract

Authors

Keywords

External Resources

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