Browsing by Subject "orthognathic surgery"

Now showing 1 - 3 of 3
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    Bone substitutes in Le Fort I osteotomy to promote bone union and skeletal stability
    (Elsevier, 2019-05) Zanettini, L.; Polido, W.; Pagnoncelli, R.; Oral and Maxillofacial Surgery and Hospital Dentistry, School of Dentistry
    Background: Maxillary advancement by Le Fort I osteotomy has become the standard procedure to restore function and facial aesthetics, correct skeletal and occlusal discrepancies and treat obstructive sleep apnea in patients with facial deformities. Incomplete ossification between the bone segments at the jaw osteotomy site has proven to be a major problem in these cases. There are several studies in the literature that address orthognathic surgery, but only a limited number that discuss the use of graft materials in maxillary osteotomy. Bone grafts were introduced in recent decades in order to promote and improve bone union and prevent the formation of gaps.
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    Is There a Correlation Between Airway Volume and Maximum Constriction Area Location in Different Dentofacial Deformities?
    (Elsevier, 2020) dos Santos, Liseane F.; Albright, David A.; Dutra, Vinicius; Bhamidipall, Surya S.; Stewart, Kelton T.; Polido, Waldemar D.; Orthodontics and Oral Facial Genetics, School of Dentistry
    Purpose The purpose of the present study was to correlate the airway volume and maximum constriction area (MCA) with the type of dentofacial deformity in patients who required orthognathic surgery. Materials and Methods The present retrospective cohort study included orthognathic surgery patients selected from the private practice of one of us. The selected cases were stratified into 5 different groups according to the clinical and cephalometric diagnosis of their dentofacial deformity. The preoperative airway volume and anatomic location of the MCA were calculated using the airway tool of the Dolphin Imaging software module (Dolphin Imaging and Management Solutions, Chatsworth, CA) and correlated with the diagnosed dentofacial deformity. Differences in the pretreatment airway volumes and MCA location were compared among the deformities. Results The MCA location was more often the nasopharynx for maxillary deficiency and the oropharynx for mandibular deficiency deformities. The nasopharynx volume was significantly smaller statistically ( P < .005) for maxillary deficiency plus mandibular excess compared with mandibular deficiency. The hypopharynx volume was significantly smaller statistically ( P < .005) for vertical maxillary excess plus mandibular deficiency than for both maxillary deficiency and maxillary deficiency plus mandibular excess. No statistically significant difference was found among the different deformity groups in relation to the mean airway volume ( P > .005). Conclusions The location of the airway MCA seems to have a strong correlation with the horizontal position of the maxilla and mandible. The MCA in maxillary deficiencies (isolated or combined) was in the nasopharynx, and the MCA in mandibular deficiencies (isolated or combined) was in the oropharynx. Clinicians should consider these anatomic findings when planning the location and magnitude of orthognathic surgery movements to optimize the outcomes.
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    A novel machine learning model for class III surgery decision
    (Springer, 2022-08) Lee, Hunter; Ahmad, Sunna; Frazier, Michael; Dundar, Mehmet Murat; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
    Purpose The primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model. Methods The sample consisted of 196 skeletal class III patients. All the cases were allocated randomly, 136 to the training set and the remaining 60 to the test set. Using the test set, the success rate of the artificial neural network model was estimated, along with a 95% confidence interval. To predict surgical cases, we trained a binary classifier using two different methods: random forest (RF) and logistic regression (LR). Results Both the RF and the LR model showed high separability when classifying each patient for surgical or non-surgical treatment. RF achieved an area under the curve (AUC) of 0.9395 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.7908 and higher bound = 0.9799. On the other hand, LR achieved an AUC of 0.937 on the test set. 95% confidence intervals were computed by bootstrap sampling as lower bound = 0.8467 and higher bound = 0.9812. Conclusions RF and LR machine learning models can be used to generate accurate and reliable algorithms to successfully classify patients up to 90%. The features selected by the algorithms coincide with the clinical features that we as clinicians weigh heavily when determining a treatment plan. This study further supports that overjet, Wits appraisal, lower incisor angulation, and Holdaway H angle can be used as strong predictors in assessing a patient’s surgical needs.