3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks

Feng, Yuan; Tao, Baoxin; Fan, Jiacheng; Wang, Shigang; Mo, Jinqiu; Wu, Yiqun; Liang, Qinghua

doi:10.1007/s11548-023-02841-1

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…A development in daily-life technology, such as smartphone application with augmented reality and depth perception, can be made to further help dentists in effectively taking a photograph of a patient, as well as measuring and calculating an appropriate anterior teeth size. Moreover, the AI and deep learning-based dental restoration methods is expected to be an effective technology to solve this problem in the future [ [21] , [22] , [23] , [24] ]. With the aid of technology, a single photograph will provide sufficient information for anterior tooth selection in complete denture fabrication.…”

Section: Discussionmentioning

confidence: 99%

Two-dimensional facial measurements for anterior tooth selection in complete denture treatment

Chunhabundit,

Prateepamornkul,

Arayapisit

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Two-dimensional facial measurements for anterior tooth selection in complete denture treatment

Chunhabundit,

Prateepamornkul,

Arayapisit

et al. 2023

Heliyon

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“…As a result, incorporating a 3D representation proves beneficial. Zhu et al ( 2022 ) and Feng et al ( 2023 ) advocated for the utilization of transformer networks in combination with dynamic graph convolutional neural networks (Wang et al, 2018 ). The input data was represented as a point cloud.…”

Section: Introductionmentioning

confidence: 99%

“…While the exact data format was not specified, voxelized points are assumed to be utilized due to the employed architecture (Wu et al, 2016 ). The selection of teeth for reconstruction varies, ranging from an arbitrary tooth (Zhu et al, 2022 ) to specific molars (Chau et al, 2023 ; Ding et al, 2023 ) or incisors (Feng et al, 2023 ). Unlike 2D methods, the input data is no longer confined to the occlusal surfaces of a limited number of teeth.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning

Broll,

Rosentritt,

Schlegl

et al. 2024

Front. Artif. Intell.

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Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.

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“…The iterative method is then used to form the crown mesh model based on the point cloud. Results indicated the promising potential of this method, as the method requires less than 11 s to reconstruct a case [ 24 ]. Despite these advancements, however, it should be noted that conventional techniques still maintain their relevance, and many technicians continue to rely on conventional crown fabrication methods.…”

Section: Introductionmentioning

confidence: 99%

Measurement of proximal contact of single crowns to assess interproximal relief: A pilot study

Kim,

Lau,

Kim

et al. 2023

Heliyon

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3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks

Cited by 7 publications

References 23 publications

Two-dimensional facial measurements for anterior tooth selection in complete denture treatment

Two-dimensional facial measurements for anterior tooth selection in complete denture treatment

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning

Measurement of proximal contact of single crowns to assess interproximal relief: A pilot study

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