Sex estimation from maxillofacial radiographs using a deep learning approach

Hiroki Hase, Yuichi Mine, Shota Okazaki, Yuki Yoshimi, Shota Ito, Tzu-Yu Peng, Mizuho Sano, Yuma Koizumi, Naoya Kakimoto, Kotaro Tanimoto, Takeshi Murayama

Research output: Contribution to journalArticlepeer-review

Abstract

Artificial intelligence (AI) has made remarkable advancements, revolutionizing domains such as medical imaging and diagnostics1,2). Deep learning, a prominent methodology within AI, has enabled automated interpretation of medical images at the level of realworld experts, under specific conditions3). In the realm of dentistry, the potential application of deep learning spans across a variety of domains including but not limited to, the detection of dental caries4), diagnosis of temporomandibular joint disorders5), prediction of the debonding probability of computer-aided design/ computer-aided manufacturing composite resin crowns6), design of removable partial dentures7) and support in implant drilling procedures8). Thus, clinical practitioners and researchers are urged to acquire proficiency in AI technologies due to their increasing significance in the field of dentistry.
Original languageEnglish
JournalDental Materials Journal
Volumeadvpub
DOIs
Publication statusAccepted/In press - Apr 11 2024

Keywords

  • Artificial intelligence
  • Deep learning
  • Sex estimation
  • Maxillofacial radiograph
  • Lateral cephalogram

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