Generative deep learning approaches for the design of dental restorations: A narrative review

URN zum Zitieren dieses Dokuments:: urn:nbn:de:bvb:355-epub-583928
DOI zum Zitieren dieses Dokuments:: 10.5283/epub.58392

Broll, Alexander ; Goldhacker, Markus

; Hahnel, Sebastian ; Rosentritt, Martin

Lizenz: Creative Commons Namensnennung 4.0 International
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Veröffentlichungsdatum dieses Volltextes: 05 Jun 2024 15:09

Alternative Links zum Volltext:DOI Verlag

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Objectives: This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep
learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is
conducted.
Data/Sources: PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to
2023.
Study selection: The review includes 9 articles published from 2018 to 2023. The selected articles showcase
novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL
methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental
plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions
focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include
antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include
Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DLbased
tooth reconstruction reports error rates approximately two times lower.
Conclusions: Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights
into patterns and structures. Through specialized application, these models reconstruct morphologically and
functionally sound dental structures, leveraging information from the existing teeth. The reported advancements
facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point
clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative
data representations like wavelets for 3D shape completion and inference problems.
Clinical significance: Generative network architectures employed in the analysis and reconstruction of dental
structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold
the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times
and diminished requirement for manual intervention may lead to cost savings and improved accessibility of
dental services.

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