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

Jun-Ho Cho; Gülce Çakmak; Yuseung Yi; Hyung-In Yoon; Burak Yilmaz; Martin Schimmel

doi:10.1016/j.jdent.2023.104830

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

J Dent. 2024 Feb:141:104830. doi: 10.1016/j.jdent.2023.104830. Epub 2023 Dec 30.

Authors

Jun-Ho Cho¹, Gülce Çakmak², Yuseung Yi¹, Hyung-In Yoon³, Burak Yilmaz⁴, Martin Schimmel²

Affiliations

¹ Department of Prosthodontics, Seoul National University Dental Hospital, Seoul, Republic of Korea.
² Department of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland.
³ 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.
⁴ 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.

PMID: 38163455
DOI: 10.1016/j.jdent.2023.104830

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.

Methods: Thirty partial arch scans of prepared posterior teeth were used. The crowns were designed using two DL-based methods (AA and AD) and a technician-based method (NC). The crown design outcomes were three-dimensionally compared, focusing on tooth morphology, internal fit, occlusion, and proximal contacts, by calculating the geometric relationship. Statistical analysis utilized the independent t-test, Mann-Whitney test, one-way ANOVA, and Kruskal-Wallis test with post hoc pairwise comparisons (α = 0.05).

Results: The AA and AD groups, with the NC group as a reference, exhibited no significant tooth morphology discrepancies across entire external or occlusal surfaces. The AD group exhibited higher root mean square and positive average values on the axial surface (P < .05). The AD and NC groups exhibited a better internal fit than the AA group (P < .001). The cusp angles were similar across all groups (P = .065). The NC group yielded more occlusal contact points than the AD group (P = .006). Occlusal and proximal contact intensities varied among the groups (both P < .001).

Conclusions: Crowns designed by using both DL-based software programs exhibited similar morphologies on the occlusal and axial surfaces; however, they differed in internal fit, occlusion, and proximal contacts. Their overall performance was clinically comparable to that of the technician-based method in terms of the internal fit and number of occlusal contact points.

Clinical significance: DL-based dental software for crown design can streamline the digital workflow in restorative dentistry, ensuring clinically-acceptable outcomes on tooth morphology, internal fit, occlusion, and proximal contacts. It can minimize the necessity of additional design optimization by dental technician.

Keywords: Computer aided design; Deep learning; Internal fit; Occlusion; Proximal contacts; Tooth morphology.

Publication types

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

MeSH terms

Ceramics
Computer-Aided Design
Crowns
Deep Learning*
Dental Marginal Adaptation
Dental Porcelain*
Dental Prosthesis Design / methods
Software

Substances

Dental Porcelain