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Morphology and mechanical performance of dental crown designed by 3D-DCGAN.
Dent Mater. 2023 03; 39(3):320-332.DM

Abstract

OBJECTIVES

This study utilised an Artificial Intelligence (AI) method, namely 3D-Deep Convolutional Generative Adversarial Network (3D-DCGAN), which is one of the true 3D machine learning methods, as an automatic algorithm to design a dental crown.

METHODS

Six hundred sets of digital casts containing mandibular second premolars and their adjacent and antagonist teeth obtained from healthy personnel were machine-learned using 3D-DCGAN. Additional 12 sets of data were used as the test dataset, whereas the natural second premolars in the test dataset were compared with the designs in (1) 3D-DCGAN, (2) CEREC Biogeneric, and (3) CAD for morphological parameters of 3D similarity, cusp angle, occlusal contact point number and area, and in silico fatigue simulations with finite element (FE) using lithium disilicate material.

RESULTS

The 3D-DCGAN design and natural teeth had the lowest discrepancy in morphology compared with the other groups (root mean square value = 0.3611). The Biogeneric design showed a significantly (p < 0.05) higher cusp angle (67.11°) than that of the 3D-DCGAN design (49.43°) and natural tooth (54.05°). No significant difference was observed in the number and area of occlusal contact points among the four groups. FE analysis showed that the 3D-DCGAN design had the best match to the natural tooth regarding the stress distribution in the crown. The 3D-DCGAN design was subjected to 26.73 MPa and the natural tooth was subjected to 23.97 MPa stress at the central fossa area under physiological occlusal force (300 N); the two groups showed similar fatigue lifetimes (F-N curve) under simulated cyclic loading of 100-400 N. Designs with Biogeneric or technician would yield respectively higher or lower fatigue lifetime than natural teeth.

SIGNIFICANCE

This study demonstrated that 3D-DCGAN could be utilised to design personalised dental crowns with high accuracy that can mimic both the morphology and biomechanics of natural teeth.

Links

Publisher Full Text (DOI)

Authors+Show Affiliations

Ding H
Dental Materials Science, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong.
Cui Z
Department of Computer Science, Faculty of Engineering, The University of Hong Kong, Hong Kong; School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
Maghami E
Department of Mechanical Engineering and Mechanics, College of Engineering, Drexel University, Philadelphia, USA.
Chen Y
Dental Materials Science, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong.
Matinlinna JP
Dental Materials Science, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong; Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, UK.
Pow EHN
Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong.
Fok ASL
Minnesota Dental Research Center for Biomaterials and Biomechanics, School of Dentistry, University of Minnesota, USA.
Burrow MF
Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong.
Wang W
Department of Computer Science, Faculty of Engineering, The University of Hong Kong, Hong Kong; Department of Visualization, College of Architecture, Texas A&M University, USA.
Tsoi JKH
Dental Materials Science, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong. Electronic address: jkhtsoi@hku.hk.

MeSH

CrownsArtificial IntelligenceDental Prosthesis DesignComputer-Aided DesignDental PorcelainAlgorithmsDental Stress Analysis

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

36822895

Citation

Ding, Hao, et al. "Morphology and Mechanical Performance of Dental Crown Designed By 3D-DCGAN." Dental Materials : Official Publication of the Academy of Dental Materials, vol. 39, no. 3, 2023, pp. 320-332.
Ding H, Cui Z, Maghami E, et al. Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dent Mater. 2023;39(3):320-332.
Ding, H., Cui, Z., Maghami, E., Chen, Y., Matinlinna, J. P., Pow, E. H. N., Fok, A. S. L., Burrow, M. F., Wang, W., & Tsoi, J. K. H. (2023). Morphology and mechanical performance of dental crown designed by 3D-DCGAN. Dental Materials : Official Publication of the Academy of Dental Materials, 39(3), 320-332. https://doi.org/10.1016/j.dental.2023.02.001
Ding H, et al. Morphology and Mechanical Performance of Dental Crown Designed By 3D-DCGAN. Dent Mater. 2023;39(3):320-332. PubMed PMID: 36822895.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Morphology and mechanical performance of dental crown designed by 3D-DCGAN. AU - Ding,Hao, AU - Cui,Zhiming, AU - Maghami,Ebrahim, AU - Chen,Yanning, AU - Matinlinna,Jukka Pekka, AU - Pow,Edmond Ho Nang, AU - Fok,Alex Siu Lun, AU - Burrow,Michael Francis, AU - Wang,Wenping, AU - Tsoi,James Kit Hon, Y1 - 2023/02/21/ PY - 2022/05/03/received PY - 2023/02/06/revised PY - 2023/02/14/accepted PY - 2023/2/24/pubmed PY - 2023/3/9/medline PY - 2023/2/23/entrez KW - 3D-DCGAN KW - Artificial Intelligence KW - CAD/CAM KW - Dental Crown KW - Design SP - 320 EP - 332 JF - Dental materials : official publication of the Academy of Dental Materials JO - Dent Mater VL - 39 IS - 3 N2 - OBJECTIVES: This study utilised an Artificial Intelligence (AI) method, namely 3D-Deep Convolutional Generative Adversarial Network (3D-DCGAN), which is one of the true 3D machine learning methods, as an automatic algorithm to design a dental crown. METHODS: Six hundred sets of digital casts containing mandibular second premolars and their adjacent and antagonist teeth obtained from healthy personnel were machine-learned using 3D-DCGAN. Additional 12 sets of data were used as the test dataset, whereas the natural second premolars in the test dataset were compared with the designs in (1) 3D-DCGAN, (2) CEREC Biogeneric, and (3) CAD for morphological parameters of 3D similarity, cusp angle, occlusal contact point number and area, and in silico fatigue simulations with finite element (FE) using lithium disilicate material. RESULTS: The 3D-DCGAN design and natural teeth had the lowest discrepancy in morphology compared with the other groups (root mean square value = 0.3611). The Biogeneric design showed a significantly (p < 0.05) higher cusp angle (67.11°) than that of the 3D-DCGAN design (49.43°) and natural tooth (54.05°). No significant difference was observed in the number and area of occlusal contact points among the four groups. FE analysis showed that the 3D-DCGAN design had the best match to the natural tooth regarding the stress distribution in the crown. The 3D-DCGAN design was subjected to 26.73 MPa and the natural tooth was subjected to 23.97 MPa stress at the central fossa area under physiological occlusal force (300 N); the two groups showed similar fatigue lifetimes (F-N curve) under simulated cyclic loading of 100-400 N. Designs with Biogeneric or technician would yield respectively higher or lower fatigue lifetime than natural teeth. SIGNIFICANCE: This study demonstrated that 3D-DCGAN could be utilised to design personalised dental crowns with high accuracy that can mimic both the morphology and biomechanics of natural teeth. SN - 1879-0097 UR - https://www.unboundmedicine.com/medline/citation/36822895/Morphology_and_mechanical_performance_of_dental_crown_designed_by_3D_DCGAN_ DB - PRIME DP - Unbound Medicine ER -