Three-Dimensional Facial Simulation in Bilateral Sagittal Split Osteotomy: A Validation Study of 100 Patients

Liebregts, Jeroen; Timmermans, Maarten; Koning, M.J.J. de; Bergé, Stefaan; Maal, T.J.J.

doi:10.1016/j.joms.2014.11.006

Cited by 35 publications

(34 citation statements)

References 32 publications

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“…The same authors evaluated Maxilim performances for soft-tissue simulation in 100 patients underwent BSSO for mandibular advancement [23]. As in the previous case, the accuracy of the simulation was assessed with two methods, a 3D cephalometric analysis and a 3D distance map for the entire face and for specific regions of interest.…”

Section: Work Based On Maxilim Software (3d)mentioning

confidence: 99%

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

et al. 2019

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Three-dimensional technologies have had a wide diffusion in several fields of application throughout the last decades; medicine is no exception and the interest in their introduction in clinical applications has grown with the refinement of such technologies. We focus on the application of 3D methodologies in maxillofacial surgery, where they can give concrete support in surgical planning and in the prediction of involuntary facial soft-tissue changes after planned bony repositioning. The purpose of this literature review is to offer a panorama of the existing prediction methods and software with a comparison of their reliability and to propose a series of still pending issues. Various software are available for surgical planning and for the prediction of tissue displacements, but their reliability is still an unknown variable in respect of the accuracy needed by surgeons. Maxilim, Dolphin and other common planning software provide a realistic result, but with some inaccuracies in specific areas of the face; it also is not totally clear how the prediction is obtained by the software and what is the theoretical model they are based on.

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Section: Work Based On Maxilim Software (3d)mentioning

confidence: 99%

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

et al. 2019

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“…This builds on the work of Liebregts et al, who showed that surfaces acquired from pre- and postoperative CT images can be used to plan mandibular advancement surgery (Liebregts et al, 2015a, b). Their study accurately predicted soft tissue changes postsurgery and was validated in another paper published by this group using the same technique to plan bimaxillary correction (Liebregts et al, 2015a, b). These papers provide important evidence supporting virtual planning of surgery but are limited to nonsyndromic adults and adolescents.…”

Section: Discussionmentioning

confidence: 95%

Combined soft and skeletal tissue modelling of normal and dysmorphic midface postnatal development

Ibrahim

Suttie

Bulstrode

et al. 2016

Journal of Cranio-Maxillofacial Surgery

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Background Midface hypoplasia as exemplified by Treacher Collins Syndrome (TCS) can impair appearance and function. Reconstruction involves multiple invasive surgeries with variable long-term outcomes. This study aims to describe normal and dysmorphic midface postnatal development through combined modelling of skeletal and soft tissues and to develop a surgical evaluation tool. Materials and methods Midface skeletal and soft tissue surfaces were extracted from computed tomography scans of 52 control and 14 TCS children, then analysed using dense surface modelling. The model was used to describe midface growth, morphology, and asymmetry, then evaluate postoperative outcomes. Results Parameters responsible for the greatest variation in midface size and shape showed differences between TCS and controls with close alignment between skeletal and soft tissue models. TCS children exhibited midface dysmorphology and hypoplasia when compared with controls. Asymmetry was also significantly higher in TCS midfaces. Combined modelling was used to evaluate the impact of surgery in one TCS individual who showed normalisation immediately after surgery but reversion towards TCS dysmorphology after 1 year. Conclusion This is the first quantitative analysis of postnatal midface development using combined modelling of skeletal and soft tissues. We also provide an approach for evaluation of surgical outcomes, laying the foundations for future development of a preoperative planning tool.

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“…The following procedure is feature extraction, which converted the input data into feature vectors. And then, we will train different models on the training set and evaluated by 10-fold cross validation as well as Split to Equal validation (SEV) [30], [31]. Finally, the trained model will be validated on independent test data, and then 5 metrics would be used to evaluate the performance of predictors.…”

Section: Methodsmentioning

confidence: 99%

K_net: Lysine Malonylation Sites Identification With Neural Network

et al. 2020

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Lysine Malonylation (Kmal) is a newly discovered protein post-translational modifications (PTMs) type, which plays an important role in many biological processes. Therefore, identifying and understanding Kmal sites is very critical in the studies of biology and diseases. The typical methods are time-wasting and expensive. Nowadays, many researchers have proposed machine learning (ML) methods to deal with PTMs's identification issue. Especially, some deep learning (DL) methods are also utilized in this field. In this work, we proposed K_net, which employed Convolutional Neural Network to identify the potential sites. Meanwhile, we proposed a new verification method Split to Equal Validation (SEV), which can well solve the impact of sample imbalance on prediction results. More Specifically, Acc, Sn, Sp, MCC and AUC values were adopted to evaluate the prediction performance of predictors. In total, CNN_Kmal achieved the better performance than other methods. INDEX TERMS Lysine malonylation, deep learning, convolutional neural network, split to equal validation.

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Three-Dimensional Facial Simulation in Bilateral Sagittal Split Osteotomy: A Validation Study of 100 Patients

Cited by 35 publications

References 32 publications

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

3D Soft-Tissue Prediction Methodologies for Orthognathic Surgery—A Literature Review

Combined soft and skeletal tissue modelling of normal and dysmorphic midface postnatal development

K_net: Lysine Malonylation Sites Identification With Neural Network

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