ArticleScientific reports2021
Development of novel artificial intelligence systems to predict facial morphology after orthognathic surgery and orthodontic treatment in Japanese patients.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Educational Applications of ChatGPT in University-Based Dental Education. A Systematic Review.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Pooled it
- Clinical applications of machine learning in predicting 3D shapes of the human body: a systematic review.BMC bioinformatics · 2022Pooled it
- Prediction of Three-Dimensional Soft Tissue Changes Following Mandibular PEEK Implantation (Proof of Concept).Journal of personalized medicine · 2026Article
- Validation of a Machine Learning-Derived Algorithm for the Measurement of Facial First Impressions.Aesthetic plastic surgery · 2026Article
- Predicting 3D Post-Orthodontic Facial Outcomes With a Diffusion Model Trained on Unpaired Datasets.International dental journal · 2026Article
- Deep Learning-Based Prediction of Orthognathic Surgery Need in Skeletal Class II Patients Using Facial Profile Images: An Artificial Intelligence Study.Journal of imaging informatics in medicine · 2026Article
- Review
- The Role of Artificial Intelligence in Orthognathic Surgery: A Scoping Review.Dentistry journal · 2026Review
- 3D Correspondence Between Maxillary Central Incisor Morphology and Facial Shape in Young Adults.Dentistry journal · 2026Article
- Deep learning for Angle classification based on intraoral photographs: an interpretability perspective.BMC oral health · 2026Article
- Can artificial intelligence optimize treatment planning and outcome prediction in fixed tooth- and implant-supported prosthodontics? A scoping review.BMC oral health · 2025Article
- Evaluation of the Performance of an Artificial Intelligence-Based Classification Model for Pediatric Maxillofacial Morphology.Diagnostics (Basel, Switzerland) · 2025Article
- Incisor position objective for favorable profile in orthodontic camouflage treatment of skeletal class II cases.Progress in orthodontics · 2025Article
- Review
- Precision and Efficacy of Digital Technology in Orthognathic Surgery for Facial Asymmetry Correction: A Quantitative Analysis.Aesthetic plastic surgery · 2025Article
- Developing an artificial intelligence-based progressive growing GAN for high-quality facial profile generation and evaluation through turing test and aesthetic analysis.Scientific reports · 2025Article
- Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications.Imaging science in dentistry · 2025Review
- Factors influencing craniofaciadental changes in skeletal Class III orthognathic surgery by using machine learning.Journal of dental sciences · 2025Article
- Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.Head & face medicine · 2025Article
- Artificial Intelligence Applications in Pediatric Craniofacial Surgery.Diagnostics (Basel, Switzerland) · 2025Review
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
From a socio-psychological standpoint, improving the morphology of the facial soft-tissues is regarded as an important therapeutic goal in modern orthodontic treatment. Currently, many of the algorithms used in commercially available software programs that are said to provide the function of performing profile prediction are based on the false assumption that the amount of movement of hard-tissue and soft-tissue has a proportional relationship. The specification of the proportionality constant value depends on the operator, and there is little evidence to support the validity of the prediction result. Thus, the present study attempted to develop artificial intelligence (AI) systems that predict the three-dimensional (3-D) facial morphology after orthognathic surgery and orthodontic treatment based on the results of previous treatment. This was a retrospective study in a secondary adult care setting. A total of 137 patients who underwent orthognathic surgery (n = 72) and orthodontic treatment with four premolar extraction (n = 65) were enrolled. Lateral cephalograms and 3-D facial images were obtained before and after treatment. We have developed two AI systems to predict facial morphology after orthognathic surgery (System S) and orthodontic treatment (System E) using landmark-based geometric morphometric methods together with deep learning methods; where cephalometric changes during treatment and the coordinate values of the faces before treatment were employed as predictive variables. Eleven-fold cross-validation showed that the average system errors were 0.94 mm and 0.69 mm for systems S and E, respectively. The total success rates, when success was defined by a system error of < 1 mm, were 54% and 98% for systems S and E, respectively. The total success rates when success was defined by a system error of < 2 mm were both 100%. AI systems to predict facial morphology after treatment were therefore confirmed to be clinically acceptable.
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