Lee et al. [13, 14, 16, 17] attempted to combine a CBCT image and laser-scanned models for evaluating the root position at different stages of orthodontic treatment and reported that tooth root position could be predicted from combination with pretreatment CBCT images and posttreatment laser-scanned model images. However, pretreatment CBCT images do not provide detailed information of crown morphology and occlusion. When interdigitation between maxillary and mandibular dentition is tight or crown restorations are present, artifacts of CBCT images occur; thus, it is difficult to integrate the two imaging modalities, i.e., CBCT and laser-scanned model imaging. Inaccuracy of integration of two imaging modalities affects final predictability of tooth root position. Therefore, in this report, from the beginning stage before orthodontic treatment, pretreatment 3D tooth models were generated from pretreatment CBCT and pretreatment intraoral scans. The tooth roots were obtained from the CBCT image, and the tooth crowns were obtained from the intraoral scans, thereby minimizing the possibility of overlapping error (integration error) due to the artifacts of the crown appearing in the CBCT image at the pretreatment stage.
Tooth segmentation is an important step for fabricating the individual tooth model, of which accurate segmentation is essential. Various computer algorithms for automatic tooth segmentation have been proposed, and some software programs for automatic segmentation have been released in dentistry. Hence, a method that isolates the tooth including the root from the alveolar bone in CBCT images without removing the alveolar bone is preferable. The software used in this study is generally used for processing medical images and creating 3D models. Unlike medical segmentation process of other anatomic structures such as the pelvic bone or heart, tooth segmentation from alveolar bone was difficult. Basically, the program performs segmentation by differentiating and taking different levels of multiple anatomic structures. However, the contrast level of the tooth and alveolar bones is similar, and differentiating between the two is hard for the software due to very narrow periodontal ligament space between tooth and alveolar bone. Thus, fully automatic segmentation was impossible for isolating tooth from the alveolar bone. The program’s automatic segmentation function, region-growing, was primarily used for rough segmentation, and it was adjusted manually for accuracy using the slice edit tool. The region-growing tool provides the capacity to split the segmentation into separate objects. Morphology operation prior to region-growing was done on all slices to take the intrinsic tooth structure from the bone. There are two options in the function for 8-connectivity and 26-connectivity; 26-connectivity was applied to select the pixels in the boundary of the structure considering neighboring pixels in 3D. One study about the segmentation method of watershed transformation reported that the proper selection of the segmentation threshold is critical for CBCT images with a low contrast and high noise level . Ye et al.  evaluated the integration accuracy of CBCT images and dental model according to segmentation threshold settings, and they found that the accuracy of the integration of laser-scanned dental models into CBCT images is higher with a high-relative Hounsfield unit threshold setting in 0.20 and 0.40 mm voxel sizes .
In order to estimate the posttreatment root position, individual tooth model at pretreatment was superimposed to the posttreatment intraoral scans. In other words, pretreatment individual tooth model which was fabricated by combining pretreatment intraoral scan and CBCT data was replaced with a posttreatment intraoral scan. Since the crown morphology is identical in pretreatment and posttreatment intraoral scan, the root position would be changed according to the position of posttreatment intraoral scan. Then, this changed root position was compared with the actual root position at posttreatment CBCT. The actual tooth model means the tooth model was fabricated using posttreatment CBCT data in each method. In other words, these actual tooth models were fabricated in each method; thus, there was a slight difference between the actual tooth models from each method.
In Table 2, the values of estimated models were smaller than those of actual tooth models. All but two of the estimated inter-arch widths were underestimated via the estimate model using the manual method. In contrast, in Table 3, using the deep learning method, three of the five maxillary inter-arch measures were over-estimated. The mandibular inter-arch measures were similar to the manual method where the estimated tooth model also underestimates the actual model. The possible reason of that was morphological changes of root apex after treatment. The actual models were fabricated using CBCT scan after treatment. The reason why there were underestimated values in the maxilla was that the morphological changes of root apex occurred more in the maxilla than in the mandible.
In the present study, there were differences around the second premolar area between estimated and actual tooth models. All study participants with premolar extraction had undergone first premolar extraction. The amount of tooth movement is generally large around extraction spaces. It is believed that these differences are due to the large amount of tooth movement in the second premolar area. Moreover, the root axis angle of mandibular incisors showed significant differences between the estimated and actual models using the automatic method. The root axis angle of mandibular incisors also showed significant differences in the comparison between the manual and automatic methods. The inter-radicular spaces around the mandibular incisors are narrow; thus, inter-radicular alveolar bone is commonly thin around the mandibular incisors. This might lead to errors in tooth segmentation process. Therefore, careful consideration of tooth segmentation is essential for this area.
In this study, the second molars were excluded, as they are rarely monitored compared to other teeth during orthodontic treatment. In addition, there are many cases in which the tooth root morphology of the second molars rather than the first molars is irregular, increasing the likelihood of errors. Furthermore, intraoral scanning inaccuracy of second molars may also lead to errors [19, 20]. However, as digital technologies in orthodontic treatment including virtual setup and indirect bonding using 3D printing have become popular, the second molars are included in orthodontic treatment from the beginning of the treatment stages. Considering this, further research including the measurements of second molars is necessary.
Regarding the reproducibility of the fabrication process of tooth models, accuracy of tooth models may be affected by the construction skill and experience of the examiners. In the present study, all processes were conducted by an experienced single researcher with over 10 years of experience in this field, who graduated from a dental school and completed a Ph.D. program in orthodontics for minimizing reproducibility errors. The tooth-modeling service of CephX was used for the deep learning automatic method using convolutional neural network features. With the advent of deep learning technology, artificial intelligence technology is showing remarkable practical effects, as it can analyze and learn like a human; recognize data in text, image, and sound format; and perform image classification, segmentation, and enhancement.
In the present study, the 3D reverse engineering software (Rapidform) was used for the integration process of the pretreatment CBCT-scanned tooth root and intraoral-scanned tooth crown. For other currently available software, there are Dental Monitoring and Geomagic software (3D Systems, USA). Geomagic software mainly provides the ability to process STL or computer-aided design (CAD) file format and is commonly used in the fields of making digital 3D models and CAD assemblies. One study regarding the accuracy of Dental Monitoring application reported that 3D digital dental models generated by the Dental Monitoring application in photograph and video modes were accurate enough to be used for clinical applications .
Deep learning offers advantages in reading medical images and diagnosing diseases. Artificial intelligence technology brings forth novel diagnostic and therapeutic systems for radiology, imaging technology, ultrasonography, and pathological diagnosis, which can improve the quality and efficiency of clinical work comprehensively. Moreover, the technology is gradually changing the traditional medical model, representing a direction and trend for future human medical development.
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