Can deep learning-designed anterior tooth-borne crown fulfill morphologic, aesthetic, and functional criteria in clinical practice?

Journal: Journal of dentistry
PMID: 39326724

Abstract

OBJECTIVES: This study aimed to compare the design outcomes of anterior crowns generated using deep learning (DL)-based software with those fabricated by a technician using conventional dental computer-assisted design (CAD) software without DL support, with a focus on the evaluation of crown morphology, function, and aesthetics.

Authors

  • Gülce Çakmak
    Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland.
  • Jun-Ho Cho
    Department of Prosthodontics, Seoul National University Dental Hospital, Seoul, Republic of Korea.
  • Jinhyeok Choi
    PhD Candidate, Department of Biomedical Sciences, Seoul National University, 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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