The tooth movement efficiency of different orthodontic thermoplastics for clear aligners: study protocol for a randomized controlled clinical trial.

INTRODUCTION: With regard to the esthetics and comfort of orthodontic treatment, the requirement for removable clear aligners (CAs) is increasing. Unlike conventional fixed orthodontic appliances, CAs were made of thermoplastic film by thermoforming on the personalized dental models. The construction of orthodontic thermoplastic is a critical factor for orthodontic tooth movement (OTM). Polyethylene terephthalate glycol-modified (PETG) and thermoplastic polyurethane (TPU) are the most commonly orthodontic thermoplastics; however, the evidence of the differences between different orthodontic thermoplastic are limited to vitro environment and the evidence in vivo environment is not available. Therefore, this trial aims to provide reliable evidence for orthodontists' personalized treatment plans whether the two most commonly used orthodontic thermoplastics of PETG and TPU have differences in the efficiency of OTM. METHODS AND ANALYSIS: This randomized controlled clinical study will recruit 44 orthodontic patients for orthodontic treatment. All the subjects will be randomized into two groups (PETG and TPU, n = 22 for each group). In the first stage (M0 to M1), clear aligners will be made of two orthodontic thermoplastics and move the maxillary first or second premolars 2 mm. In the second stage, patients will take the standard orthodontic treatments. The primary outcome will be the efficiency of clear aligners made of different materials on the digital models. The secondary outcome will be the efficiency of clear aligners made of different materials on the cone-beam computed tomography (CBCT). The efficiency will be calculated through the superimposition of the digital models and CBCT. DISCUSSION: The results from this trial will serve as evidence for orthodontists and manufacturers and clarify whether the difference in orthodontic thermoplastics significantly impacts the efficiency of OTM. TRIAL REGISTRATION NUMBER: ChiCTR2300070980. Registered on 27 April 2023. https://www.chictr.org.cn/showproj.html?proj=186253.

[Application of Deep Learning in Differential Diagnosis of Ameloblastoma and Odontogenic Keratocyst Based on Panoramic Radiographs].

Objective To evaluate the accuracy of different convolutional neural networks (CNN),representative deep learning models,in the differential diagnosis of ameloblastoma and odontogenic keratocyst,and subsequently compare the diagnosis results between models and oral radiologists. Methods A total of 1000 digital panoramic radiographs were retrospectively collected from the patients with ameloblastoma (500 radiographs) or odontogenic keratocyst (500 radiographs) in the Department of Oral and Maxillofacial Radiology,Peking University School of Stomatology.Eight CNN including ResNet (18,50,101),VGG (16,19),and EfficientNet (b1,b3,b5) were selected to distinguish ameloblastoma from odontogenic keratocyst.Transfer learning was employed to train 800 panoramic radiographs in the training set through 5-fold cross validation,and 200 panoramic radiographs in the test set were used for differential diagnosis.Chi square test was performed for comparing the performance among different CNN.Furthermore,7 oral radiologists (including 2 seniors and 5 juniors) made a diagnosis on the 200 panoramic radiographs in the test set,and the diagnosis results were compared between CNN and oral radiologists. Results The eight neural network models showed the diagnostic accuracy ranging from 82.50% to 87.50%,of which EfficientNet b1 had the highest accuracy of 87.50%.There was no significant difference in the diagnostic accuracy among the CNN models (P=0.998,P=0.905).The average diagnostic accuracy of oral radiologists was (70.30±5.48)%,and there was no statistical difference in the accuracy between senior and junior oral radiologists (P=0.883).The diagnostic accuracy of CNN models was higher than that of oral radiologists (P<0.001). Conclusion Deep learning CNN can realize accurate differential diagnosis between ameloblastoma and odontogenic keratocyst with panoramic radiographs,with higher diagnostic accuracy than oral radiologists.

Detection of vertical root fractures by cone-beam computed tomography based on deep learning.

OBJECTIVES: This study aims to evaluate the performance of ResNet models in the detection of in vitro and in vivo vertical root fractures (VRF) in Cone-beam Computed Tomography (CBCT) images. METHODS: A CBCT image dataset consisting of 28 teeth (14 intact and 14 teeth with VRF, 1641 slices) from 14 patients, and another dataset containing 60 teeth (30 intact and 30 teeth with VRF, 3665 slices) from an in vitro model were used for the establishment of VRFconvolutional neural network (CNN) models. The most popular CNN architecture ResNet with different layers was fine-tuned for the detection of VRF. Sensitivity, specificity, accuracy, PPV (positive predictive value), NPV (negative predictive value), and AUC (the area under the receiver operating characteristic curve) of the VRF slices classified by the CNN in the test set were compared. Two oral and maxillofacial radiologists independently reviewed all the CBCT images of the test set, and intraclass correlation coefficients (ICCs) were calculated to assess the interobserver agreement for the oral maxillofacial radiologists. RESULTS: The AUC of the models on the patient data were: 0.827(ResNet-18), 0.929(ResNet-50), and 0.882(ResNet-101). The AUC of the models on the mixed data get improved as:0.927(ResNet-18), 0.936(ResNet-50), and 0.893(ResNet-101). The maximum AUC were: 0.929 (0.908-0.950, 95% CI) and 0.936 (0.924-0.948, 95% CI) for the patient data and mixed data from ResNet-50, which is comparable to the AUC (0.937 and 0.950) for patient data and (0.915 and 0.935) for the mixed data obtained from the two oral and maxillofacial radiologists, respectively. CONCLUSIONS: Deep-learning models showed high accuracy in the detection of VRF using CBCT images. The data obtained from the in vitro VRF model increases the data scale, which is beneficial to the training of deep-learning models.

Dehiscence and fenestration of Class I individuals with normality patterns in the anterior region: a CBCT study.

OBJECTIVES: The purpose of this study was to investigate the prevalence of alveolar bone dehiscence and fenestration of Class I individuals with normality patterns in the anterior region using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: A total of 4715 retrospective cases from January 2018 to December 2020 in the Orthodontic Department of xxx Hospital were screened. Sixty-one cases were Class I individuals with normality patterns in the anterior region. Their incidence of dehiscence and fenestration in the anterior teeth region was studied and statistically analyzed. RESULTS: Dehiscence was found in 27.46% of the evaluated anterior teeth and fenestration was found in 26.91% of anterior teeth. Severe dehiscences and fenestrations mainly occurred in mandibular canines and maxillary canines, respectively. Alveolar bone defects were present in 100% of patients, while one patient had alveolar bone defects in 91.67% of the anterior teeth. CONCLUSIONS: Dehiscence was found in 27.46% of the anterior teeth of Class I individuals with normality patterns, while fenestration was found in 26.91% of them. Alveolar bone defects were present in 100% of patients. CLINICAL RELEVANCE: Alveolar bone dehiscence and fenestration were normal and common in our sample, indicating that they are more likely to be physiological rather than pathological defects. Orthodontists should be aware of the presence and severity of these defects before treatment in order to avoid both possible complications and overtreatment.

Diagnostic efficacy of CBCT, MRI and CBCT-MRI fused images in determining anterior disc displacement and bone changes of temporomandibular joint.

OBJECTIVES: To evaluate the diagnostic efficacy of CBCT-MRI fused image for anterior disc displacement and bone changes of temporomandibular joint (TMJ), which are the main imaging manifestations of temporomandibular disorders (TMD). METHODS: Two hundred and thirty-one TMJs of 120 patients who were diagnosed with TMD were selected for the study. The anterior disc displacement, bone defect and bone hyperplasia evaluated by three experts were used as a reference standard. Three residents individually evaluated all the three sets of images, which were CBCT images, MRI images and CBCT-MRI fused images from individual CBCT and MRI images in a random order for the above-mentioned three imaging manifestations with a five-point scale. Each set of images was observed at least 1 week apart. A second evaluation was performed 4 weeks later. Intra- and interobserver agreements were assessed using the intraclass correlation coefficient (ICC). The areas under the ROC curves (AUCs) of the three image sets were compared with a Z test, and p < 0.05 was considered statistically significant. RESULTS: One hundred and forty-five cases were determined as anterior disc displacement, 84 cases as bone defect and 40 cases as bone hyperplasia. The intra- and interobserver agreements in the CBCT-MRI fused image set (0.76-0.91) were good to excellent, and the diagnostic accuracy for bone changes was significantly higher than that of MRI image set (p＜0.05). CONCLUSIONS: CBCT-MRI fused images can display the disc and surrounding bone structures simultaneously and significantly improve the observers' reliability and diagnostic accuracy, especially for inexperienced residents.

Diagnostic efficacy of CBCT, MRI, and CBCT-MRI fused images in distinguishing articular disc calcification from loose body of temporomandibular joint.

OBJECTIVES: To evaluate the diagnostic efficacy of CBCT-MRI fused images for articular disc calcification of temporomandibular joint (TMJ). MATERIALS AND METHODS: Twenty patients (24 TMJs) whose image examinations showed dense bodies in the TMJ space were included in the study. The locations of dense bodies evaluated by the three experts were used as a reference standard. Three oral and maxillofacial radiology residents evaluated whether the dense bodies were disc calcification or not, with a five-point scale for four sets of images (CBCT alone, MRI alone, both CBCT and MRI observed at a time, and CBCT-MRI fused images) randomly and independently. Each set of images was observed at least 1 week apart. A second evaluation was performed after 4 weeks. Intraclass correlation coefficients were calculated to assess the intra- and inter-observer agreement. The areas under the ROC curves (AUCs) were compared between the four image sets using Z test. RESULTS: Ten cases were determined as articular disc calcifications, and fourteen cases were recognized as loose bodies in the TMJ spaces. The average AUC index for the CBCT-MRI fused images was 0.95 and significantly higher than the other sets (p < 0.01). The intra- and inter-observer agreement in the CBCT-MRI fused images (0.90-0.91, 0.93) was excellent and higher than those in the other images. CONCLUSIONS: CBCT-MRI fused images can significantly improve the observers' reliability and accuracy in determining articular disc calcification of the TMJ. CLINICAL RELEVANCE: The multimodality image fusion is feasible in detecting articular disc calcification of the TMJ which are hard to define by CBCT or MRI alone. It can be utilized especially for inexperienced residents to shorten the learning curve and improve diagnostic accuracy.