Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.


Materials and methods
Subjects. This study was approved by the Institutional Review Board of Yonsei University Dental Hospital (No. 2-2021-0043) and was conducted in accordance with ethical regulations and guidelines. The requirement for informed consent was waived since this was a retrospective study and all data in this study were used after anonymization.
The training and validation of the AI model used the panoramic radiographs of 612 patients with an impacted mesiodens in the anterior maxillary region who visited Yonsei University Dental Hospital from July 2017 to January 2021. The panoramic images were acquired from two types of equipment: RAYSCAN Alpha (Ray Co., Ltd., Hwaseong-si, Korea) and PaX-i3D Green (Vatech Co., Ltd., Hwaseong-si, Korea). The panoramic images were divided into three groups according to the stage of tooth development, as follows: primary dentition (3-6 years), mixed dentition (7-13 years), and permanent dentition (over 14 years old). Table 1 shows the detailed distribution of the training and validation dataset. Since mesiodens is more difficult to diagnose in primary and mixed dentition than in permanent dentition, panoramic radiographs were collected mainly with primary and mixed dentition.
The test dataset consisted of internal data from Yonsei University Dental Hospital and external data obtained using ProMax (Planmeca Inc., Helsinki, Finland) from Gangnam Severance Hospital. The internal test dataset consisted of 65 images with mesiodens and 65 images without mesiodens, and the external test dataset consisted of 58 images with mesiodens and 60 images without mesiodens. Table 2 shows the test dataset. All panoramic images were selected from patients who had cone-beam computed tomography scans, which were used to confirm the presence of mesiodens.
Image preparation and preprocessing. Panoramic radiographs were downloaded in the bitmap format with a matrix size of approximately 2996 (width) × 1502 (height) pixels. As preprocessing, the CLAHE method was applied to all the original images. The CLAHE method involves applying equalization based on dividing the image into several regions of almost equal sizes 19 . Applying the CLAHE method has been found to improve the image quality compared to other histogram equalization methods, and it has been widely used in deep learning model studies based on medical images [20][21][22][23] . The experiments using the original images and CLAHE images were implemented in Windows 10 with the TensorFlow 1.16.0 framework on an NVIDIA GPU (TITAN RTX).
Development and evaluation of the model. We developed a model based on YOLOv3 for detecting mesiodens. YOLO is a representative deep learning (DL) detection algorithm that has shown much better performance than other detection algorithms 24 . This model required information on the training dataset (i.e., the location and class name of ground truth) for model training. An oral radiologist with over 20 years of experience performed annotation using a rectangular region of interest (ROI) including just the mesiodens as a gold standard using the graphical image annotation tool LabelImg (version 1.8.4, available at https:// github. com/ tzuta lin/ label Img). From the annotations, the coordinates of the upper left (X 1 , Y 1 ) and lower right (X 2 , Y 2 ) corners of the ROI were determined and its class name was extracted as "mesiodens" (Fig. 1). The information extracted from the input images was used in the model training process.
The panoramic images were resized to 512 (width) × 256 (height) pixels and input to the backbone (darknet-53 architecture), which consisted of 53 convolutional layers, with batch normalization added to all convolutional layers. The leak rectified linear unit (ReLU) was used as the activation function. The output comprised three feature maps that passed through the convolutional layers, and mesiodens was automatically detected at different resolutions through these three feature maps. When the model detected mesiodens, it provided an image marked with a red box in the detected area, and if there was no detected mesiodens, it provided the input panoramic  www.nature.com/scientificreports/ image without a box (Fig. 2). It was judged that the model correctly predicted mesiodens when the intersection over union (IoU) value of the detected mesiodens area was 0.5 or higher 25 . The first step of model training used the weights of the YOLO model pre-trained using the COCO dataset 24 and the model was trained for 300 epochs with our dataset. In the initial 150 epochs, only the weights of the last 3 layers were trained with our dataset, and in the next 150 epochs, the weights of the entire network were trained on our dataset. In the original images, the detection performance of the proposed model was evaluated in terms of accuracy, sensitivity, and specificity using internal and external test datasets. The results with and without the CLAHE method were compared for the primary, mixed, and permanent groups. The oral radiologist annotated the mesiodens with a yellow rectangular box. The coordinate information of the upper left (X 1 , Y 1 ) and the lower right (X 2 , Y 2 ) was determined, and the class name was extracted as "mesiodens. "   Confusion matrices of the model using the internal and external test datasets according to the dentition group are shown in Fig. 3. Table 4 shows the accuracy, sensitivity, and specificity of the model using internal and external test datasets depending on whether the CLAHE preprocessing method was applied. The accuracy and specificity of the original images was higher than that of the CLAHE images for both the internal and external test datasets. Figure 4 shows examples of mesiodens correctly detected by the developed model. False-positive and falsenegative cases are presented in Fig. 5. The incorrect detection cases were confused with the succeeding tooth germ, anterior nasal spine, and ala of the nose.

Discussion
AI research has been conducted in a diverse range of fields, and numerous studies have also been conducted in the dental field. DL algorithms with convolutional neural networks (CNNs) have received considerable attention and have been used for segmentation, classification, and detection with panoramic radiographs 17,21,26 . Many studies have applied these techniques to orthodontic diagnoses, root canal treatment, tooth extraction, and the diagnosis of lesions 27,28 . The most common application has been for classifying cysts and tumors of the jaw 17,29,30 .
Recently, various algorithms such as the YOLO algorithm, deformable parts model, and R-CNN algorithm have been introduced and found to effectively detect cancer, nodules, fractures, or other lesions on medical images [31][32][33][34] . The YOLO algorithm was first proposed by Redmon et al. 24 , and it has been applied in the dental field to detect various diseases on panoramic radiographs 17,29,35,36 . Yang et al. 35 detected cysts and tumors of the jaw using YOLOv2 and obtained a precision of 0.707 and recall of 0.680. Kwon et al. 17  Early AI research tended to use images obtained from a single device at one institution [37][38][39] , making the resulting models difficult to generalize, and the problem arose that performance deteriorated when AI models were applied in actual clinical practice. The assessment of the real-world clinical performance of a model-based DL algorithm requires external validation using data collected at institutions other than the institution that provided the training data 40 . In particular, multi-center research is especially important for panoramic radiography because the thickness of the image layer is different for each type of equipment, and blurring and overlapping can vary depending on the equipment and patient position. We conducted training and validation using images obtained with two types of equipment, and tested the model with images obtained from different devices at internal and external institutions to confirm generalizability. The accuracy, sensitivity, and specificity of the developed model using the internal test dataset were all more than 95%, and the corresponding performance metrics with the external test dataset showed only slightly poorer performance compared with the internal test results, with values of 89.8%, 87.9%, and 91.7%, respectively. Previous studies that performed external testing generally showed similar trends. In a DL study for the diagnosis of mandibular condylar fractures using panoramic www.nature.com/scientificreports/ radiographs from two hospitals, when images from the same hospital were used as the training and test data sets, the accuracy was 80.4% and 81%, respectively. In contrast, when images from different hospitals were used, the accuracy decreased to 59.0% and 60% 21 . This drop-off in performance is thought to be due to variation between the training panoramic radiographs and the external panoramic radiographs (e.g., differences in image noise and brightness), as mentioned in other research 41 .
Due to the geometric configuration of panoramic radiographs, the maxillary anterior region is the most blurred, and diseases or mesiodens in this area is often missed. Kuwada et al. 13 developed DL models (AlexNet, VGG-16, and DetectNet) and compared their performance. A limitation of that previous study is that it was performed on permanent dentition only, with panoramic radiographs from one institution, and without external testing. In contrast, our study developed a model using the YOLOv3 algorithm and compared its performance according to preprocessing. Furthermore, our study was conducted with all dentition groups (primary, mixed, and permanent dentition) on panoramic radiographs and was tested internally and externally using multi-center www.nature.com/scientificreports/ data. Our proposed model showed high accuracy (more than 93%) in primary and mixed dentition, as well as permanent dentition, and the accuracy, sensitivity, and specificity showed good performance (over 87%) for the external test dataset. The CLAHE preprocessing method was also applied to investigate whether preprocessing improved model performance. Rahman et al. 42 studied five image enhancement techniques, including CLAHE, to detect COVID-19 on chest X-rays, all of which showed very reliable performance. Some studies have applied CLAHE to panoramic radiography 17,18 . In the present study, the original images without CLAHE had higher accuracy, sensitivity, and specificity, both internally and externally, except for sensitivity in internal testing. Therefore, the CLAHE preprocessing method had a negligible effect on the performance of the model, and preprocessing should be carefully considered during model development.
Our study has limitations in that the number of samples used was small and that multiple mesiodens were not included. Although it is currently very challenging to collect external data related to this topic, further research using imaging data from more centers and devices will improve the performance of the model.

Conclusion
The developed CNN model for fully automatic detection of mesiodens showed high performance in multi-center tests for all types of dentition, including primary, mixed, and permanent dentition. The developed model has the potential to help dental clinicians diagnose mesiodens on panoramic radiographs.

Data availability
The data generated and analyzed during the current study are not publicly available due to privacy laws and policies in Korea, but are available from the corresponding author on reasonable request.