Tooth morphology, internal fit, occlusion and proximal contacts of dental crowns designed by deep learning-based dental software: A comparative study.

Journal: Journal of dentistry
PMID: 38163455

Abstract

OBJECTIVES: This study compared the tooth morphology, internal fit, occlusion, and proximal contacts of dental crowns automatically generated via two deep learning (DL)-based dental software systems with those manually designed by an experienced dental technician using conventional software.

Authors

  • Jun-Ho Cho
    Department of Prosthodontics, Seoul National University Dental Hospital, Seoul, Republic of Korea.
  • Gülce Çakmak
    Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland.
  • Yuseung Yi
    Department of Prosthodontics, Seoul National University Dental Hospital, Seoul, Republic of Korea.
  • Hyung-In Yoon
    Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland; Department of Prosthodontics, School of Dentistry and Dental Research Institute, Seoul National University, Seoul, Republic of Korea. Electronic address: drhiy226@snu.ac.kr.
  • Burak Yilmaz
    Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland; Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland; Division of Restorative and Prosthetic Dentistry, The Ohio State University, Columbus, OH, USA.
  • Martin Schimmel
    Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland.

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