To date, for the diagnosis of dentofacial dysmorphosis, we have relied almost entirely on reference points, planes, and angles. This is time consuming, and it is also greatly influenced by the skill level of the practitioner. To solve this problem, we wanted to know if deep neural networks could predict postoperative results of orthognathic surgery without relying on reference points, planes, and angles. We use three-dimensional point cloud data of the skull of 269 patients. The proposed method has two main stages for prediction. In step 1, the skull is divided into six parts through the segmentation network. In step 2, three-dimensional transformation parameters are predicted through the alignment network. The ground truth values of transformation parameters are calculated through the iterative closest points (ICP), which align the preoperative part of skull to the corresponding postoperative part of skull. We compare pointnet, pointnet++ and pointconv for the feature extractor of the alignment network. Moreover, we design a new loss function, which considers the distance error of transformed points for a better accuracy. The accuracy, mean intersection over union (mIoU), and dice coefficient (DC) of the first segmentation network, which divides the upper and lower part of skull, are 0.9998, 0.9994, and 0.9998, respectively. For the second segmentation network, which divides the lower part of skull into 5 parts, they were 0.9949, 0.9900, 0.9949, respectively. The mean absolute error of transverse, anterior–posterior, and vertical distance of part 2 (maxilla) are 0.765 mm, 1.455 mm, and 1.392 mm, respectively. For part 3 (mandible), they were 1.069 mm, 1.831 mm, and 1.375 mm, respectively, and for part 4 (chin), they were 1.913 mm, 2.340 mm, and 1.257 mm, respectively. From this study, postoperative results can now be easily predicted by simply entering the point cloud data of computed tomography.

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.

In this study, we evaluated changes in the masseter and lateral pterygoid muscles in the prognathic mandible group after a mandibular setback by comparing the volume-to-length ratios. Preoperative and postoperative 1-year computed tomography was used to calculate the volume-to-length ratio of the lateral pterygoid and masseter muscle in 60 Korean individuals. Three-dimensional images were reconstructed, the results of which showed no significant differences in the volume-to-length ratios of the masseter and lateral pterygoid muscles after a mandibular setback (p > 0.05). This result was found for both vertical ramus osteotomy and sagittal split ramus osteotomy, and for both males and females. No significant differences in the volume-to-length ratio of the masseter and lateral pterygoid muscles were found up to 1 year after a mandibular setback. Therefore, this study can contribute to the prediction of soft-tissue profiles after mandibular setback.

Mandibular prognathism is one of the most concerning subjects in the oral and maxillofacial fields. In our previous studies, we attempted to clarify the etiology of mandibular prognathism. They revealed that one of the major characteristics of mandibular prognathism was the lower volume/length ratio of the mandibular condyle and body compared to normal, and the masseter muscle showed parallelism with this. This study aimed to evaluate the relationship between mandibular prognathism and the lateral pterygoid muscle by measuring the orientation and volume/length ratio of the lateral pterygoid muscle. Computed tomography was used to calculate the volume/length ratio of the lateral pterygoid muscle in 60 Korean individuals. Mimics 10.0 and Maya version 2018 were used to reconstruct the surface area and surface planes. The results showed that the prognathic group showed smaller lateral pterygoid volume/length ratios compared to the normal group (p < 0.05). In addition, the normal group displayed a larger horizontal angle (p < 0.05) to the mandibular and palatal planes than the prognathic group. This demonstrated that the mechanical drawback of the lateral pterygoid in the prognathic group is associated with mandibular prognathism.

Background: Quick and large-scale segmentation along with three-dimensional (3D) reconstruction is necessary to make precise 3D musculoskeletal models for surface anatomy education, palpation training, medical communication, morphology research, and virtual surgery simulation. However, automatic segmentation of the skin and muscles remain undeveloped. Materials and methods: Therefore, in this study, we developed workflows for semi-automatic segmentation and surface reconstruction, using rotoscoping and warping techniques. Results: The techniques were applied to multi detector computed tomography images, which were optimized to quickly generate surface models of the skin and the anatomical structures underlying the fat tissue. Conclusions: The workflows developed in this study are expected to enable researchers to create segmented images and optimized surface models from any set of serially sectioned images quickly and conveniently. Moreover, these optimized surface models can easily be modified for further application or educational use. Background: Quick and large-scale segmentation along with three-dimensional (3D) reconstruction is necessary to make precise 3D musculoskeletal models for surface anatomy education, palpation training, medical communication, morphology research, and virtual surgery simulation. However, automatic segmentation of the skin and muscles remain undeveloped. Materials and methods: Therefore, in this study, we developed workflows for semi-automatic segmentation and surface reconstruction, using rotoscoping and warping techniques. Results: The techniques were applied to multi detector computed tomography images, which were optimized to quickly generate surface models of the skin and the anatomical structures underlying the fat tissue. Conclusions: The workflows developed in this study are expected to enable researchers to create segmented images and optimized surface models from any set of serially sectioned images quickly and conveniently. Moreover, these optimized surface models can easily be modified for further application or educational use.

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