Evidence map›Paper›PMID 34090256›Full record

ArticleMedical image analysis2021

A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect.

Daeseung Kim, Tianshu Kuang, Yriu L Rodrigues, Jaime Gateno, Steve G F Shen, Xudong Wang, Kirhyn Stein, Hannah H Deng, Michael A K Liebschner, James J Xia

Abstract read
In one paragraph

Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. AI-assisted mesh generation for subject-specific modeling of facial soft tissues.International journal of computer assisted radiology and surgery · 2025
    Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.International journal of computer assisted radiology and surgery · 2022
    Article
  12. 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

10 authors.

Daeseung KimDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA.
Tianshu KuangDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA.
Yriu L RodriguesDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA.
Jaime GatenoDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, 407 E 61st St, New York, NY 10065, USA.
Steve G F ShenDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, 639 Zhi-Zao-Ju Road, Shanghai 200011, China.
Xudong WangDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University College of Medicine, 639 Zhi-Zao-Ju Road, Shanghai 200011, China.
Kirhyn SteinDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA.
Hannah H DengDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA.
Michael A K LiebschnerDepartment of Neurosurgery, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA. Electronic address: mliebschner@rimkus.com.
James J XiaDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, 6560 Fannin St, Houston, TX 77030, USA; Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, 407 E 61st St, New York, NY 10065, USA. Electronic address: jxia@HoustonMethodist.org.

Funding

Outcome-Driven Approach to Minimize the Risks of Facial Distortion Following CMF SurgeryR01DE021863 · NIDCR · METHODIST HOSPITAL RESEARCH INSTITUTE · PI GATENO, JAIME · 2013 to 2025
$5.1M
A Novel Imaging Informatics Platform for Craniomaxillofacial SurgeryR01DE022676 · NIDCR · METHODIST HOSPITAL RESEARCH INSTITUTE · PI GATENO, JAIME · 2011 to 2020
$4.0M
Learning-Based Approach for Personalized Craniomaxillofacial Surgical PlanningR01DE027251 · NIDCR · METHODIST HOSPITAL RESEARCH INSTITUTE · PI GATENO, JAIME · 2017 to 2021
$2.9M
NIDCR NIH HHS R01 DE021863NIDCR NIH HHS R01 DE022676NIDCR NIH HHS R01 DE027251
6 · The paper itself

Abstract

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect along with the mucosa sliding effect. Finally, the orthognathic surgery initiated soft-tissue change is simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. Our method was quantitatively validated using 35 retrospective clinical data sets by comparing it to the traditional FEM simulation method and the FEM simulation method with mucosa sliding effect only. The surface deviation error of our method showed significant improvement in the upper and lower lips over the other two prior methods. In addition, the evaluation results using our lip-shape analysis, which reflects clinician's qualitative evaluation, also proved significant improvement of the lip prediction accuracy of our method for the lower lip and both upper and lower lips as a whole compared to the other two methods. In conclusion, the prediction accuracy in the clinically critical region, i.e., the lips, significantly improved after applying incremental simulation with realistic lip sliding effect compared with the FEM simulation methods without the lip sliding effect.

Indexed as

LipOrthognathic SurgeryCephalometryHumansMandibleMaxillaRetrospective StudiesFacial soft-tissue-change predictionFinite element methodLip sliding effectOrthognathic surgery

Identifiers

PMID34090256
PMCPMC8316331

What OpenQuestion holds

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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.