Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning

Ma, Lei; Kim, Daeseung; Xiao, Deqiang; Kuang, Tianshu; Liu, Qin; Lang, Yankun; Deng, Hannah H.; Gateño, Jaime; Wu, Ye; Yang, Erkun; Liebschner, Michael A. K.; Xia, James J.; Yap, Pew Thian

doi:10.1007/978-3-030-87202-1_44

Cited by 6 publications

(6 citation statements)

References 19 publications

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“…The sample sizes within the studies ranged from five to 137 patients. Three studies reported surgical intervention types such as bimaxillary surgery, mandibular advancement, and maxillary advancement surgeries ( 22 , 24 , 26 ), while four studies did not ( 25 , 27 - 29 ). AI models like Artificial Neural Networks (ANN) and machine learning methods like Logistic Regression (LR), Ridge Regression (RR), and Least Absolute Shrinkage and Selection Operator (LASSO) are used in the articles.…”

Section: Resultsmentioning

confidence: 99%

Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review

Almarhoumi

2024

J Clin Exp Dent

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Background Accurate prediction of facial soft tissue changes post-orthognathic surgery is crucial for treatment planning and patient communication. Current models pose limitations due to the complexity of facial biomechanics and individual variances. Artificial intelligence (AI) has emerged as an important tool in many disciplines, including the dental field. Objectives The aim of this scoping review is to assess the accuracy of AI in predicting facial changes post-orthognathic surgery in comparison to traditional models. Explore the strengths and limitations of the current AI models. Material and Methods Following PRISMA-DTA guidelines, a comprehensive search was conducted manually and through Medline, Embase, Web of Science, Scopus, and Google Scholar databases was conducted, focusing on studies that applied AI models with various machine learning and deep learning algorithms for post-surgical outcome prediction. Selection criteria were based on the PICO format, emphasizing studies that compared AI-predicted outcomes with actual post-surgical results. Literature was searched until January 31, 2024. Results The initial search result yielded 1579 records. After screening and assessment for eligibility, seven studies met the inclusion criteria, with publication dates ranging from 2009 to 2023. Several AI algorithms were evaluated on different orthognathic surgical procedures, revealing the high predictive accuracy of AI models across various facial regions. Conclusions AI demonstrates significant potential for enhancing the precision of facial outcome predictions following orthognathic surgery. However, despite the promising results, limitations such as small sample sizes and a lack of external validation were noted. Further research with larger, more diverse datasets and standardized validation methods is essential for optimizing AI’s clinical utility. Key words: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Orthognathic Surgery, Facial Soft-tissue Prediction, Predictive Accuracy, Orthodontics.

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Section: Resultsmentioning

confidence: 99%

Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review

Almarhoumi

2024

J Clin Exp Dent

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“…Anatomic landmarks are automatically identified and localized [59,60] with paired right and left structures used to generate both lateral cephalometric and frontal symmetry analysis. Surgical correction is performed by the computer utilizing specific algorithms with operator input (supervised learning) or without (unsupervised learning) [61][62][63]. Once completed the operator can determine the clinical appropriateness and feasibility of the AI generated approach and accept, modify or reject as necessary.…”

Section: Future: Pt 2: Digital Treatment Planning Artificial Intellig...mentioning

confidence: 99%

Orthognathic surgery past, present, and future

Conley

2022

Clinical and Investigative Orthodontics

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Orthognathic surgery has been performed for over two centuries with the first procedure occurring in the 1860s. It was not until the late 1950s with the pioneering work of Obwegeser that the modern orthognathic surgery era began. From the beginning, oral surgeons and orthodontists have worked in parallel fashion; advances in both disciplines have led to what is now routinely performed. New and exciting developments are underway not only in diagnosis but also in treatment planning with computer aided surgical simulation and the use of artificial intelligence (AI) to enhance patient treatment outcomes. The purpose of this article is to briefly review the history of orthognathic surgery, to examine the present status and to highlight emerging technologies and advancements.

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“…To train the network, we adopt a hybrid loss function, Loss = L shape + αL density +βL LP T , to compute the difference between P F −post and P F −post . The loss includes a shape loss L shape [19] to minimize the distance between prediction and target shape, a point density loss L density [19] to measure the similarity between prediction and target shape, and a local-point-transform (LPT) loss L LP T [9] to constraint relative movements between one point and its neighbors.…”

Section: Attentive Correspondence Assisted Movement Transformationmentioning

confidence: 99%

“…The segmentation of facial soft tissue and the bones were completed automatically using deep learning-based SkullEngine segmentation tool [7], and the surface models were reconstructed using Marching Cube [8]. In order to retrospectively recreate the surgical plan that could "achieve" the actual postoperative outcomes, the postoperative facial and bony surface models were registered to the preoperative ones based on surgically unaltered bony volumes, i.e., cranium, and used as a roadmap [9]. Virtual osteotomies were then reperformed on preoperative bones.…”

Section: Datasetmentioning

confidence: 99%

“…Especially, in-depth modeling of the correlation between bony and facial surfaces is the key factor for accurate facial change prediction using deep learning technology. Ma et al [9] proposed a facial appearance change simulation network, FC-Net, that embedded the bony-facial relationship into facial appearance simulation. FC-Net takes both bony and facial point sets as input to jointly infer the facial change following the bony movement.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

Fang

Kim

et al. 2022

Lecture Notes in Computer Science

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Simulating facial appearance change following bony movement is a critical step in orthognathic surgical planning for patients with jaw deformities. Conventional biomechanics-based methods such as the finite-element method (FEM) are labor intensive and computationally inefficient. Deep learning-based approaches can be promising alternatives due to their high computational efficiency and strong modeling capability. However, the existing deep learning-based method ignores the physical correspondence between facial soft tissue and bony segments and thus is significantly less accurate compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to estimate the facial appearance by transforming the bony movement to facial soft tissue through a point-to-point attentive correspondence matrix. Experimental results on patients with jaw deformity show that our proposed method can achieve comparable facial change prediction accuracy compared with the state-of-the-art FEMbased approach with significantly improved computational efficiency.

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Deep Simulation of Facial Appearance Changes Following Craniomaxillofacial Bony Movements in Orthognathic Surgical Planning

Cited by 6 publications

References 19 publications

Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review

Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review

Orthognathic surgery past, present, and future

Deep Learning-Based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement

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