ArticleThe Angle orthodontist2019
Predicting soft tissue changes after orthognathic surgery:
Article in The Angle orthodontist, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- What amount of data is required to develop artificial intelligence that can accurately predict soft tissue changes after orthognathic surgery?The Angle orthodontist · 2025Article
- Predicting nepetalactone accumulation in Nepeta persica using machine learning algorithms and geospatial analysis.Scientific reports · 2025Article
- Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.Nature communications · 2025Article
- Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.The Angle orthodontist · 2025Article
- Factors influencing the development of artificial intelligence in orthodontics.Orthodontics & craniofacial research · 2024Article
- Predicting optimal patient-specific postoperative facial landmarks for patients with craniomaxillofacial deformities.International journal of oral and maxillofacial surgery · 2024Article
- Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.The Angle orthodontist · 2024Article
- Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.The Angle orthodontist · 2024Article
- Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?The Angle orthodontist · 2024Article
- Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.The Angle orthodontist · 2024Article
- Effects of Maxillary Movements on Lips Following Orthognathic Surgery: A Retrospective Non-Randomized Clinical Trial.International archives of otorhinolaryngology · 2024Article
- Predicting Optimal Patient-Specific Postoperative Facial Landmarks for Patients with Craniomaxillofacial Deformities.medRxiv : the preprint server for health sciences · 2023Article
- Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method.The Angle orthodontist · 2022Article
- Accuracy of three-dimensional soft tissue profile prediction in orthognathic surgery.Oral and maxillofacial surgery · 2022Article
- Evaluation of an automated superimposition method based on multiple landmarks for growing patients.The Angle orthodontist · 2022Article
- Evaluation of automated cephalometric analysis based on the latest deep learning method.The Angle orthodontist · 2021Article
- How much deep learning is enough for automatic identification to be reliable?The Angle orthodontist · 2020Article
- Evaluation of an automated superimposition method for computer-aided cephalometrics.The Angle orthodontist · 2020Article
- Automated identification of cephalometric landmarks:The Angle orthodontist · 2020Article
- Automated identification of cephalometric landmarks:The Angle orthodontist · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop a prediction algorithm for soft tissue changes after orthognathic surgery that would result in accurate predictions (1) regardless of types or complexity of operations and (2) with a minimum number of input variables. MATERIALS AND
methodsThe subjects consisted of 318 patients who had undergone the surgical correction of Class II or Class III malocclusions. Two multivariate methods-the partial least squares (PLS) and the sparse partial least squares (SPLS) methods-were used to construct prediction equations. While the PLS prediction model included 232 input variables, the SPLS method included a reduced number of variables generated by a handicapping algorithm via the sparsity control. The accuracy between the PLS and SPLS models was compared.
resultsThere were no significant differences in prediction accuracy depending on surgical movements, the sex of the subjects, or additional surgeries. The predictive performance with a reduced set of 34 input variables chosen using the SPLS method was statistically indistinguishable from the full set of variables with the original PLS prediction model.
conclusionsThe prediction method proposed in the present study was accurate for a wide range of orthognathic surgeries. A reduced set of input variables could be selected through the SPLS method while simultaneously maintaining a prediction level that was as accurate as that of the original PLS prediction model.
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