Evidence map›Paper›PMID 36754949›Full record

ArticleInternational journal of computer assisted radiology and surgery2023

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

Yuan Feng, BaoXin Tao, JiaCheng Fan, ShiGang Wang, JinQiu Mo, YiQun Wu, QingHua Liang

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Article in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Yuan Feng *School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
BaoXin Tao *Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, China.
JiaCheng FanSchool of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
ShiGang WangSchool of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
JinQiu MoSchool of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
YiQun WuDepartment of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, China. yiqunwu@hotmail.com.
QingHua LiangSchool of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China. qhliang@sjtu.edu.cn.
Shanghai Jiao Tong University · CNShanghai Ninth People's Hospital · CN

Funding

National Key R&D Program of China 2017YFB1302901
6 · The paper itself

Abstract

purposeThe design of a maxillary anterior tooth crown is crucial to post-treatment aesthetic appearance. Currently, the design is performed manually or by semi-automatic methods, both of which are time-consuming. As such, automatic methods could improve efficiency, but existing automatic methods ignore the relationships among crowns and are primarily used for occlusal surface reconstruction. In this study, the authors propose a novel method for automatically reconstructing a three-dimensional model of the maxillary anterior tooth crown.

methodA pose estimation network (PEN) and a shape estimation network (SEN) are developed for jointly estimating the crown point cloud. PEN is a regression network used for estimating the crown pose, and SEN is based on an encoder-decoder architecture and used for estimating the initial crown point cloud. First, SEN adopts a transformer encoder to calculate the shape relationship among crowns to ensure that the shape of the reconstructed point cloud is precise. Second, the initial point cloud is subjected to pose transformation according to the estimated pose. Finally, the iterative method is used to form the crown mesh model based on the point cloud.

resultThe proposed method is evaluated on a dataset with 600 cases. Both SEN and PEN are converged within 1000 epochs. The average deviation between the reconstructed point cloud and the ground truth of the point cloud is 0.22 mm. The average deviation between the reconstructed crown mesh model and the ground truth of the crown model is 0.13 mm.

conclusionThe results show that the proposed method can automatically and accurately reconstruct the three-dimensional model of the missing maxillary anterior tooth crown, which indicates the method has promising application prospects. Furthermore, the reconstruction time takes less than 11 s for one case, demonstrating improved work efficiency.

Indexed as

Imaging, Three-DimensionalToothCrownsHumansMaxillaTooth CrownMaxillary anterior tooth crown reconstructionPose and shape decouplingPose estimation networkShape estimation network

Identifiers

PMID36754949
OpenAlexW4319455577

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.