Evidence map›Paper›PMID 28570001›Full record

ArticleMedical physics2017

A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery.

Daeseung Kim, Dennis Chun-Yu Ho, Huaming Mai, Xiaoyan Zhang, Steve G F Shen, Shunyao Shen, Peng Yuan, Siting Liu, Guangming Zhang, Xiaobo Zhou and 3 more

Abstract read
In one paragraph

Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Review
  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. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning.International journal of computer assisted radiology and surgery · 2022
    Article
  14. Review
  15. Article
  16. Article
  17. Article
  18. A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019
    Article
  19. Article
  20. Article
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

13 authors.

Daeseung KimDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Dennis Chun-Yu HoDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Huaming MaiDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Xiaoyan ZhangDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Steve G F ShenDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University College of Medicine, Shanghai, 200011, China.
Shunyao ShenDepartment of Oral and Craniomaxillofacial Surgery, Shanghai Ninth People's Hospital, Shanghai Jiaotong University College of Medicine, Shanghai, 200011, China.
Peng YuanDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Siting LiuDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Guangming ZhangDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27101, USA.
Xiaobo ZhouDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, 27101, USA.
Jaime GatenoDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.
Michael A K LiebschnerDepartment of Neurosurgery, Baylor College of Medicine, Houston, TX, 77030, USA.
James J XiaDepartment of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, 77030, USA.

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
NIDCR NIH HHS R01 DE021863NIDCR NIH HHS R01 DE022676
6 · The paper itself

Abstract

purposeIt is clinically important to accurately predict facial soft-tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips, and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction.

methodsIn Stage One, soft-tissue change was simulated, using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft-tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect, using a nodal force constraint method.

resultsThe results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any subregions thereof were below 1.5 mm. The overall soft-tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods.

conclusionThe FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach.

Indexed as

Computer SimulationFaceImaging, Three-DimensionalOrthognathic Surgical ProceduresHumansMandibleMaxillaRetrospective Studiesfacial soft-tissue change predictionfinite element modelorthognathic surgerysoft-tissue modelingsoft-tissue sliding effect

Identifiers

PMID28570001
PMCPMC5553697

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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.