Rotation tendency of the posteriorly displaced proximal segment after vertical ramus osteotomy

Park, Jong Chan; Lee, Jun; Lim, Hun Jun; Kim, Bong-Chul

doi:10.1016/j.jcms.2018.09.027

Cited by 15 publications

(10 citation statements)

References 27 publications

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“…We also thought about the possibility that the muscular difference is due to the secondary effect of mandibular difference. However, it is well-known that mandibular prognathism is affected by both heredity and environmental influences [31,32]. In addition, there are many studies or examples related to the action of soft tissue on hard tissue.…”

Section: Discussionmentioning

confidence: 99%

Anatomical Characteristics of the Masseter Muscle in Mandibular Prognathism

et al. 2021

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Mandibular prognathism causes functional and esthetic problems. Therefore, many studies have been conducted to understand its etiology. Following our previous study, which revealed that the major characteristic of the mandible with prognathism is the volume/length ratio of the mandibular body and condyle, we analyzed the volume and orientation of the masseter muscle, which inserts into the mandibular body, expecting that the difference in the size of the masseter muscle causes the difference in the mandibular size. This study compared the masseter muscle of the participants in the prognathic group to those in the normal group on the volume/length ratio and orientation. The masseter muscle ratios (volume/length); the angle between the superficial and deep head of the masseter muscle; and the three planes (the palatal, occlusal, and mandibular) were analyzed. A total of 30 participants constituted the normal group (male: 15, female: 15) and 30 patients, the prognathic group (male: 15, female: 15). The results showed that the volume/length ratio of the masseter of the normal group was greater than that of the prognathic group (p < 0.05). In addition, the orientation of both the superficial and deep head of the masseter of the participants in the normal group was more vertical with respect to the mandibular plane than that of the prognathic group (p < 0.05). We concluded that the mechanical disadvantage of the masseter muscle of the prognathic group is attributed to mandibular prognathism.

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Section: Discussionmentioning

confidence: 99%

Anatomical Characteristics of the Masseter Muscle in Mandibular Prognathism

et al. 2021

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“…A maxillofacial skeletal analysis is an important part of diagnosis and treatment planning [ 13 ], and it is used to assess the vertical, lateral, and anteroposterior positions of the jaws using posteroanterior (PA) and lateral (Lat) cephalogram. Accurate diagnosis has been based on proper landmark identification by cephalogram and it is essential to a successful treatment [ 13 , 14 ]. The application of deep learning algorithms to cephalometric analysis has been studied, and many approaches have focused on the detection of cephalometric landmarks [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals

et al. 2021

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Background Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. Methods The cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients—Class ll: 221, Class lll: 312, Facial asymmetry: 89). Group II (218 patients—Class ll: 23, Class lll: 135, Facial asymmetry: 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 4:1:5. PyTorch was used as the framework for the experiment. Results Subsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively. Conclusion It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.

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“…Until now, orthodontics and orthognathic surgery have mainly relied on linear and angular measurements for the diagnosis and the planning of the therapeutic procedures [ 1 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. These measurements depend on the identification of several landmarks on cephalometric images, which are then applied to define the aforementioned measurements [ 1 , 3 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. It is well recognized that the relation between these metrics varies with the type of bite and therefore is different in skeletal classes I, II, and III [ 1 , 7 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Dentofacial dysmorphosis exhibits various aspects such as prognathism, retrognathism, maxillary hypoplasia, and asymmetry [1,2]. For their treatment, several techniques of orthognathic surgery or orthodontics are applied [2][3][4]. Meanwhile, the stomatognathic system is composed of static and dynamic structures, and its harmonious functioning is based on the balanced relationship between them [5].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography

et al. 2021

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The aim of this study was to reveal cranio-spinal differences between skeletal classification using convolutional neural networks (CNNs). Transverse and longitudinal cephalometric images of 832 patients were used for training and testing of CNNs (365 males and 467 females). Labeling was performed such that the jawbone was sufficiently masked, while the parts other than the jawbone were minimally masked. DenseNet was used as the feature extractor. Five random sampling crossvalidations were performed for two datasets. The average and maximum accuracy of the five crossvalidations were 90.43% and 92.54% for test 1 (evaluation of the entire posterior–anterior (PA) and lateral cephalometric images) and 88.17% and 88.70% for test 2 (evaluation of the PA and lateral cephalometric images obscuring the mandible). In this study, we found that even when jawbones of class I (normal mandible), class II (retrognathism), and class III (prognathism) are masked, their identification is possible through deep learning applied only in the cranio-spinal area. This suggests that cranio-spinal differences between each class exist.

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Rotation tendency of the posteriorly displaced proximal segment after vertical ramus osteotomy

Cited by 15 publications

References 27 publications

Anatomical Characteristics of the Masseter Muscle in Mandibular Prognathism

Anatomical Characteristics of the Masseter Muscle in Mandibular Prognathism

Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals

Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography

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