Evidence map›Paper›PMID 33711187›Full record

ArticleOrthodontics & craniofacial research2021

3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation.

Xiaoyu Wang, Matthew Pastewait, Tai-Hsien Wu, Chunfeng Lian, Beatriz Tejera, Yan-Ting Lee, Feng-Chang Lin, Li Wang, Dinggang Shen, Song Li and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in Orthodontics & craniofacial research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 33 citations in OpenAlex.

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  11. A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023
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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

11 authors at 7 institutions in 3 countries.

Xiaoyu WangDepartment of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
Matthew PastewaitUnited States Air Force, Kadena AB, Kadena, Japan.
Tai-Hsien WuDivision of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA.ORCID https://orcid.org/0000-0001-6844-7587
Chunfeng LianSchool of Mathematics and Statistics, Xi'an Jiaotong University, Shaanxi, China.
Beatriz TejeraOrthodontics, Nova Southeastern University, Ft. Lauderdale, FL, USA.
Yan-Ting LeeOral and Craniofacial Health Sciences Research, Adam School of Dentistry, University of North Carolina, Chapel Hill, NC, USA.
Feng-Chang LinDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
Li WangDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
Dinggang ShenDepartment of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
Song LiDepartment of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.
Ching-Chang KoDivision of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA.
University of North Carolina at Chapel Hill · USCapital Medical University · CNThe Ohio State University · USKorea University · KRNova Southeastern University · USUnited States Air Force · USXi'an Jiaotong University · CN

Funding

Engineering novel bio-inspired materials for stem-cell mediated bone regenerationR01DE022816 · NIDCR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KO, CHING-CHANG · 2014 to 2018
$2.0M
National Science Foundation 1938533NIDCR NIH HHS DE022816NIDCR NIH HHS R01 DE022816
6 · The paper itself

Abstract

objectiveThis study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol. SETTING AND SAMPLE POPULATION: Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.52 years (SD = 3.27 years; range of 8-18 years). MATERIALS AND

methodsThe deep-learning-based protocol was used to segment the maxilla and defect initially, followed by manual refinement. Paired t-tests were performed to characterize the maxillary asymmetry. A multiple linear regression was carried out to investigate the relationship between the defect parameters and those of the cleft side of the maxilla.

resultsThe cleft side of the maxilla demonstrated a significant decrease in maxillary volume and length as well as alveolar length, anterior width, posterior width, anterior height and posterior height. A significant increase in maxillary anterior width was demonstrated on the cleft side of the maxilla. There was a close relationship between the defect parameters and those of the cleft side of the maxilla.

conclusionsBased on the 3D volumetric segmentations, significant hypoplasia of the maxilla on the cleft side existed in the pyriform aperture and alveolar crest area near the defect. The defect structures appeared to contribute to the variability of the maxilla on the cleft side.

Indexed as

Cleft LipCleft PalateDeep LearningSpiral Cone-Beam Computed TomographyAdolescentChildCone-Beam Computed TomographyFemaleHumansMaleMaxilla3D measurementCBCT segmentationcleft palatedeep learning

Identifiers

PMID33711187
PMCPMC8435046
OpenAlexW3207354813

What OpenQuestion holds

Textmetadata
LicenceTDM
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.