Evidence map›Paper›PMID 38280038›Full record

ArticleClinical oral investigations2024

Deep learning for virtual orthodontic bracket removal: tool establishment and application.

Ruomei Li, Cheng Zhu, Fengting Chu, Quan Yu, Di Fan, Ningjuan Ouyang, Yu Jin, Weiming Guo, Lunguo Xia, Qiping Feng and 1 more

Abstract read
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In one paragraph

Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.5field-weighted citation impact, top 22% 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
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  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Ruomei LiDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Cheng ZhuDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Fengting ChuDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Quan YuDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Di FanSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Ningjuan OuyangDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Yu JinDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Weiming GuoDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China.
Lunguo XiaDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China. xialunguo@hotmail.com.
Qiping FengDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China. orthofeng@163.com.
Bing FangDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, 500 Quxi Road, Shanghai, 200011, China. fangbing@sjtu.edu.cn.
Shanghai Jiao Tong University · CNShanghai Ninth People's Hospital · CN

Funding

Clinical Research Plan of SHDC SHDC2020CR3009ACSA Clinical Research Fund CSA-O2020-04Innovative research team of high-level local universities in Shanghai SHSMU-ZLCX20212402Innovative Research Team of High-level Local Universities in Shanghai SSMU-ZDCX20180902Science and Technology Commission of Shanghai Municipality 19441906200Shanghai Rising-Star Program 19QA1405200Young Doctor Collaborative Innovation Team of Ninth People's Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine QC2018-02
6 · The paper itself

Abstract

objectiveWe aimed to develop a tool for virtual orthodontic bracket removal based on deep learning algorithms for feature extraction from bonded teeth and to demonstrate its application in a bracket position assessment scenario. MATERIALS AND

methodsOur segmentation network for virtual bracket removal was trained using dataset A, containing 978 bonded teeth, 20 original teeth, and 20 brackets generated by scanners. The accuracy and segmentation time of the network were tested by dataset B, which included an additional 118 bonded teeth without knowing the original tooth morphology. This tool was then applied for bracket position assessment. The clinical crown center, bracket center, and orientations of separated teeth and brackets were extracted for analyzing the linear distribution and angular deviation of bonded brackets.

resultsThis tool performed virtual bracket removal in 2.9 ms per tooth with accuracies of 98.93% and 97.42% (P < 0.01) in datasets A and B, respectively. The tooth surface and bracket characteristics were extracted and used to evaluate the results of manually bonded brackets by 49 orthodontists. Personal preferences for bracket angulation and bracket distribution were displayed graphically and tabularly.

conclusionsThe tool's efficiency and precision are satisfactory, and it can be operated without original tooth data. It can be used to display the bonding deviation in the bracket position assessment scenario. CLINICAL SIGNIFICANCE: With the aid of this tool, unnecessary bracket removal can be avoided when evaluating bracket positions and modifying treatment plans. It has the potential to produce retainers and orthodontic devices prior to tooth debonding.

Indexed as

Deep LearningDental BondingOrthodontic BracketsDental DebondingMicroscopy, Electron, ScanningArtificial IntelligenceDirect bonding techniqueNeural networksOrthodontic bracket position evaluationOrthodontic(s)

Identifiers

PMID38280038
OpenAlexW4391281593

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.