Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning

Cheng, Mengjia; Zhang, Xu; Wang, Jun; Yang, Yang; Li, Meng; Zhao, Hanjiang; Huang, Jing‐Yang; Zhang, Chenglong; Qian, Dianwei; Yu, Hongbo

doi:10.1186/s12903-023-02844-z

Cited by 7 publications

(4 citation statements)

References 16 publications

(33 reference statements)

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“…After excluding 379 duplicates, 321 articles were selected for title and abstract review, resulting in 17 articles for full-text review ( Figure 1 ). Of the 17 articles, 4 studies were excluded, because their sample was less than 100 [ 21 , 22 , 23 , 24 ], and 5 studies were excluded, because their objectives did not include the facial diagnosis applied for orthognathic surgery using artificial intelligence, ultimately including 8 studies for descriptive and methodological analysis.…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Ravelo,

Acero,

Fuentes-Zambrano

et al. 2024

JPM

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AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis

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Section: Resultsmentioning

confidence: 99%

“…Cheng et al [ 21 ], in their study, proposed a neural network to define facial deformities and the potential use for orthognathic surgical needs; they showed the benefit for diagnosis, as well as for planning orthognathic surgery. In this study, they included an observation of maxillary movements and their effect on soft tissues.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Ravelo,

Acero,

Fuentes-Zambrano

et al. 2024

JPM

View full text Add to dashboard Cite

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“…Technological advances [40][41][42][43] in the use of finite-element method (FEM) simulations, the prediction of orthognathic surgical plans from 3D cephalometric analysis via deep learning, image-based biomechanical models of the musculoskeletal system, tomographic studies, conventional study models, surgical guides, and assisted surgery are excellent in the hands of professionals prepared to use their wisdom with the most advanced tools.…”

Section: Discussionmentioning

confidence: 99%

Sixty Years of Innovation in Biomechanical Orthognathic Surgery: The State of the Art and Future Directions

Andreucci

2024

Osteology

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Craniofacial surgery is proposed and performed for a variety of reasons, ranging from congenital or acquired malformations to emotional disorders and parafunctions of the masticatory, respiratory, auditory, and visual systems. Surgery of the mandible and its orthostatic repositioning is the most common of these corrections of craniofacial anomalies. Throughout the history of these procedures, various techniques have been proposed and perfected, but always with a high rate of minor and major complications. The recurrence rate of mandibular malposition is high, as is the temporary loss of facial sensitivity and motor skills. These outcomes are often related to the choice of surgical technique rather than the skill of the surgeon, which is considered to be one of the most important factors in the final outcome. Surgical techniques involving direct manipulation of the vascular-nervous bundles, such as bilateral sagittal split osteotomy, clearly present the possibility of major or minor complications. In this study, an orthognathic surgical technique, performed by the same team for over 40 years and now available through a 20-year postoperative patient follow-up study, is presented with a literature review relating it to biomechanical concepts and bone remodeling to analyze the evolution of orthognathic surgery since it became common practice to correct maxillofacial discrepancies. In this review, we also present a case report in which previous orthodontic treatment prepared a patient for surgical correction of mandibular bone discrepancy without the need for combined maxillary and/or genioplasty, and we describe the most commonly used techniques today, as well as their advantages and disadvantages. The combination of established concepts together promotes favorable stability of mandibular osteotomies, functional anatomical positioning of the temporomandibular joint, reduced risk of injury to the mandibular vasculo-nervous bundle, and good aesthetics with positive patient acceptance and no relapse, thus these are the objectives for proposing innovative treatments that combine the technologies available today.

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“…Furthermore, these models significantly reduce computation times, enabling rapid feedback and multiple simulations, thereby enhancing clinical workflows and patient experience. Additionally, the application of AI and DL in surgical planning provides a cost-effective alternative to conventional treatment simulations, potentially leading to better patient outcomes ( 34 , 35 ).…”

Section: Discussionmentioning

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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Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning

Cited by 7 publications

References 16 publications

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Sixty Years of Innovation in Biomechanical Orthognathic Surgery: The State of the Art and Future Directions

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

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