Evidence map›Paper›PMID 29321613›Full record

ArticleScientific reports2018

Evaluation of in vivo digital root reconstruction based on anatomical characteristics of the periodontal ligament using cone beam computed tomography.

Chenxin Wang, Yi Liu, Siwei Wang, Yong Wang, Yijiao Zhao

Abstract read
In one paragraph

Article in Scientific reports, 2018. 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
–field-weighted citation impact
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.

  1. Article
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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

5 authors.

Chenxin WangCenter of Digital Dentistry, Peking University School and Hospital of Stomatology, Beijing Shi, China.
Yi LiuDepartment of Orthodontics, Peking University School and Hospital of Stomatology, Beijing Shi, China.
Siwei WangDepartment of Orthodontics, Peking University School and Hospital of Stomatology, Beijing Shi, China.
Yong WangCenter of Digital Dentistry, Peking University School and Hospital of Stomatology, Beijing Shi, China.
Yijiao ZhaoCenter of Digital Dentistry, Peking University School and Hospital of Stomatology, Beijing Shi, China. kqcadcs@bjmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study's aim was to develop and validate an approach to automatically extract and reconstruct three-dimensional (3D) digital root models from in vivo teeth based on the anatomical characteristics of the periodontal ligament using cone beam computed tomography (CBCT) data. Prior to undergoing dental extractions for orthodontic purposes, the CBCT data of each study participant were collected and imported into Mimics software to reconstruct 3D in vivo digital root models (test models). Twenty roots of 17 teeth extracted from the study's participants were scanned using a dental scanner to obtain 3D in vitro digital root models (reference models). The 3D morphological deviation between the reference and test models was compared; the 3D size of the bucco-lingual, mesio-distal, and root length dimensions were calculated. This approach achieved an average 3D morphological deviation of 0.21 mm, and the average size error in the bucco-lingual, mesio-distal, and root length dimensions were -0.35 mm, -0.17 mm, and 0.47 mm, respectively. This new automatic extraction approach rapidly and accurately reconstructs 3D in vivo root models with detailed morphological information, and has the potential to improve diagnostic and treatment work flow in orthodontic clinics, as well as in other areas of dentistry.

Indexed as

Cone-Beam Computed TomographyImaging, Three-DimensionalAlgorithmsFemaleHumansMaleModels, AnatomicPeriodontal LigamentTooth Root

Identifiers

PMID29321613
PMCPMC5762886

What OpenQuestion holds

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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.