Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence model using a cyclic generative adversarial network.

OBJECTIVES: The objective of this study was to enhance the visibility of soft tissues on cone-beam computed tomography (CBCT) using a CycleGAN network trained on CT images. METHODS: Training and evaluation of the CycleGAN were conducted using CT and CBCT images collected from Aichi Gakuin University (α facility) and Osaka Dental University (ß facility). Synthesized images (sCBCT) output by the CycleGAN network were evaluated by comparing them with the original images (oCBCT) and CT images, and assessments were made using histogram analysis and human scoring of soft-tissue anatomical structures and cystic lesions. RESULTS: The histogram analysis showed that on sCBCT, soft-tissue anatomical structures showed significant shifts in voxel intensity toward values resembling those on CT, with the mean values for all structures approaching those of CT and the specialists' visibility scores being significantly increased. However, improvement in the visibility of cystic lesions was limited. CONCLUSIONS: Image synthesis using CycleGAN significantly improved the visibility of soft tissue on CBCT, with this improvement being particularly notable from the submandibular region to the floor of the mouth. Although the effect on the visibility of cystic lesions was limited, there is potential for further improvement through refinement of the training method.

Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.

Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs.

Purpose: The aim of this study was to clarify the influence of training with a different kind of lesion on the performance of a target model. Materials and Methods: A total of 310 patients (211 men, 99 women; average age, 47.9±16.1 years) were selected and their panoramic images were used in this study. We created a source model using panoramic radiographs including mandibular radiolucent cyst-like lesions (radicular cyst, dentigerous cyst, odontogenic keratocyst, and ameloblastoma). The model was simulatively transferred and trained on images of Stafne's bone cavity. A learning model was created using a customized DetectNet built in the Digits version 5.0 (NVIDIA, Santa Clara, CA). Two machines (Machines A and B) with identical specifications were used to simulate transfer learning. A source model was created from the data consisting of ameloblastoma, odontogenic keratocyst, dentigerous cyst, and radicular cyst in Machine A. Thereafter, it was transferred to Machine B and trained on additional data of Stafne's bone cavity to create target models. To investigate the effect of the number of cases, we created several target models with different numbers of Stafne's bone cavity cases. Results: When the Stafne's bone cavity data were added to the training, both the detection and classification performances for this pathology improved. Even for lesions other than Stafne's bone cavity, the detection sensitivities tended to increase with the increase in the number of Stafne's bone cavities. Conclusion: This study showed that using different lesions for transfer learning improves the performance of the model.

Detection of unilateral and bilateral cleft alveolus on panoramic radiographs using a deep-learning system.

OBJECTIVES: The purpose of this study was to evaluate the difference in performance of deep-learning (DL) models with respect to the image classes and amount of training data to create an effective DL model for detecting both unilateral cleft alveoli (UCAs) and bilateral cleft alveoli (BCAs) on panoramic radiographs. METHODS: Model U was created using UCA and normal images, and Model B was created using BCA and normal images. Models C1 and C2 were created using the combined data of UCA, BCA, and normal images. The same number of CAs was used for training Models U, B, and C1, whereas Model C2 was created with a larger amount of data. The performance of all four models was evaluated with the same test data and compared with those of two human observers. RESULTS: The recall values were 0.60, 0.73, 0.80, and 0.88 for Models A, B, C1, and C2, respectively. The results of Model C2 were highest in precision and F-measure (0.98 and 0.92) and almost the same as those of human observers. Significant differences were found in the ratios of detected to undetected CAs of Models U and C1 (p = 0.01), Models U and C2 (p < 0.001), and Models B and C2 (p = 0.036). CONCLUSIONS: The DL models trained using both UCA and BCA data (Models C1 and C2) achieved high detection performance. Moreover, the performance of a DL model may depend on the amount of training data.

Transfer learning in diagnosis of maxillary sinusitis using panoramic radiography and conventional radiography.

OBJECTIVES: To clarify the performance of transfer learning with a small number of Waters' images at institution B in diagnosing maxillary sinusitis, based on a source model trained with a large number of panoramic radiographs at institution A. METHODS: The source model was created by a 200-epoch training process with 800 training and 60 validation datasets of panoramic radiographs at institution A using VGG-16. One hundred and eighty Waters' and 180 panoramic image patches with or without maxillary sinusitis at institution B were enrolled in this study, and were arbitrarily assigned to 120 training, 20 validation, and 40 test datasets, respectively. Transfer learning of 200 epochs was performed using the training and validation datasets of Waters' images based on the source model, and the target model was obtained. The test Waters' images were applied to the source and target models, and the performance of each model was evaluated. Transfer learning with panoramic radiographs and evaluation by two radiologists were undertaken and compared. The evaluation was based on the area of receiver-operating characteristic curves (AUC). RESULTS: When using Waters' images as the test dataset, the AUCs of the source model, target model, and radiologists were 0.780, 0.830, and 0.806, respectively. There were no significant differences between these models and the radiologists, whereas the target model performed better than the source model. For panoramic radiographs, AUCs were 0.863, 0.863, and 0.808, respectively, with no significant differences. CONCLUSIONS: This study performed transfer learning using a small number of Waters' images, based on a source model created solely from panoramic radiographs, resulting in a performance improvement to 0.830 in diagnosing maxillary sinusitis, which was equivalent to that of radiologists. Transfer learning is considered a useful method to improve diagnostic performance.

Deep-learning systems for diagnosing cleft palate on panoramic radiographs in patients with cleft alveolus.

OBJECTIVES: The aim of the present study was to create effective deep learning-based models for diagnosing the presence or absence of cleft palate (CP) in patients with unilateral or bilateral cleft alveolus (CA) on panoramic radiographs. METHODS: The panoramic images of 491 patients who had unilateral or bilateral cleft alveolus were used to create two models. Model A, which detects the upper incisor area on panoramic radiographs and classifies the areas into the presence or absence of CP, was created using both object detection and classification functions of DetectNet. Using the same data for developing Model A, Model B, which directly classifies the presence or absence of CP on panoramic radiographs, was created using classification function of VGG-16. The performances of both models were evaluated with the same test data and compared with those of two radiologists. RESULTS: The recall, precision, and F-measure were all 1.00 in Model A. The area under the receiver operating characteristic curve (AUC) values were 0.95, 0.93, 0.70, and 0.63 for Model A, Model B, and the radiologists, respectively. The AUCs of the models were significantly higher than those of the radiologists. CONCLUSIONS: The deep learning-based models developed in the present study have potential for use in supporting observer interpretations of the presence of cleft palate on panoramic radiographs.

Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques.

OBJECTIVE: The aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs. STUDY DESIGN: The panoramic radiographs containing the mandibular canal and impacted third molar were collected from 2 hospitals (Hospitals A and B). A total of 3200 areas were used for creating and evaluating learning models. A source model was created using the data from Hospital A, simulatively transferred to Hospital B, and trained using various amounts of data from Hospital B to create target models. The same data were then applied to the target models to calculate the Dice coefficient, Jaccard index, and sensitivity. RESULTS: The performance of target models trained using 200 or more data sets was equivalent to that of the source model tested using data obtained from the same hospital (Hospital A). CONCLUSIONS: Sufficiently qualified models could delineate the mandibular canal in relation to an impacted third molar on panoramic radiographs using a segmentation technique. Transfer learning appears to be an effective method for creating such models using a relatively small number of data sets.

Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.

OBJECTIVES: The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data. METHODS: In total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model. The performance was evaluated using 200 images from Hospital A (internal validity test) and 200 images from Hospital B (external validity test). RESULTS: Although the analysis of performance determined with data from Hospital B showed low recall (sensitivity), compared with the performance determined with data from Hospital A, both performances were above 80%. Precision (positive predictive value) was lower when test data from Hospital A were used for the position of anterior disc displacement. According to the intra-articular TMD classification, the proportions of accurately assigned TMJs were higher when using images from Hospital A than when using images from Hospital B. CONCLUSION: The segmentation deep learning model created in this study may be useful for identifying disc positions on MR images.

Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle.

OBJECTIVE: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities. METHODS: Panoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: For internal validity, high classification performance was obtained, with AUC values of >0.85. Conversely, external validity for the data sets from the two hospitals exhibited low performance. Using combined data sets from both hospitals, the DL model exhibited high performance, which was slightly superior or equal to that of the internal validity but without a statistically significant difference. CONCLUSION: The constructed DL model can be clinically employed for diagnosing fractures of the mandibular condyle using panoramic radiographs. However, the domain shift phenomenon should be considered when generalizing DL systems.

Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study.

OBJECTIVES: This study aimed to examine the performance of deep learning object detection technology for detecting and identifying maxillary cyst-like lesions on panoramic radiography. METHODS: Altogether, 412 patients with maxillary cyst-like lesions (including several benign tumors) were enrolled. All panoramic radiographs were arbitrarily assigned to the training, testing 1, and testing 2 datasets of the study. The deep learning process of the training images and labels was performed for 1000 epochs using the DetectNet neural network. The testing 1 and testing 2 images were applied to the created learning model, and the detection performance was evaluated. For lesions that could be detected, the classification performance (sensitivity) for identifying radicular cysts or other lesions were examined. RESULTS: The recall, precision, and F-1 score for detecting maxillary cysts were 74.6%/77.1%, 89.8%/90.0%, and 81.5%/83.1% for the testing 1/testing 2 datasets, respectively. The recall was higher in the anterior regions and for radicular cysts. The sensitivity was higher for identifying radicular cysts than for other lesions. CONCLUSIONS: Using deep learning object detection technology, maxillary cyst-like lesions could be detected in approximately 75-77%.

Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system.

Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

Cone-beam computed tomography classification of the mandibular second molar root morphology and its relationship to panoramic radiographic appearance.

OBJECTIVES: This study aimed to clarify the relationship between the panoramic radiographic appearance and the longitudinal cone-beam computed tomography (CBCT) classification of root configurations of the mandibular second molar. METHODS: Panoramic radiographs of 1058 mandibular second molars were classified into five types according to the number and configuration of the roots. These molars were also examined with CBCT at four levels between the pulp chamber and the root apex, and axial images perpendicular to the root axis were categorized into three patterns: single (fused root with small grooves on both buccal and lingual sides or a round root with one canal); double (two separate roots with a trabecular appearance between them); and C-shaped (root with a deep groove opening only on the lingual or buccal side relative to the opposite side). Based on these patterns and their scan levels, the CBCT root morphology appearance in each tooth unit was classified into seven groups. Relationships were investigated between these seven CBCT groups and the five panoramic root types. RESULTS: In panoramic types 1 and 2 (with separate roots), 85% had roots with a double pattern (groups II and III) on the CBCT images. In panoramic types 3 and 4 (with fused roots), 85% had C-shaped CBCT patterns at the lower scan levels. CONCLUSIONS: When panoramic images show fused root types, CBCT examinations should be planned to clarify the root canal configuration.

Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs.

OBJECTIVE: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses. METHODS: The imaging data for healthy maxillary sinuses (587 sinuses, Class 0), inflamed maxillary sinuses (416 sinuses, Class 1), cysts of maxillary sinus regions (171 sinuses, Class 2) were assigned to training, testing 1, and testing 2 data sets. A learning process of 1000 epochs with the training images and labels was performed using DetectNet, and a learning model was created. The testing 1 and testing 2 images were applied to the model, and the detection sensitivities and the false-positive rates per image were calculated. The accuracies, sensitivities and specificities were determined for distinguishing the inflammation group (Class 1) and cyst group (Class 2) with respect to the healthy group (Class 0). RESULTS: Detection sensitivities of healthy (Class 0) and inflamed (Class 1) maxillary sinuses were 100% for both testing 1 and testing 2 data sets, whereas they were 98 and 89% for cysts of the maxillary sinus regions (Class 2). False-positive rates per image were nearly 0.00. Accuracies, sensitivities and specificities for diagnosis maxillary sinusitis were 90-91%, 88-85%, and 91-96%, respectively; for cysts of the maxillary sinus regions, these values were 97-100%, 80-100%, and 100-100%, respectively. CONCLUSION: Deep learning could reliably detect the maxillary sinuses and identify maxillary sinusitis and cysts of the maxillary sinus regions. ADVANCES IN KNOWLEDGE: This study using a deep leaning object detection technique indicated that the detection sensitivities of maxillary sinuses were high and the performance of maxillary sinus lesion identification was ≧80%. In particular, performance of sinusitis identification was ≧90%.

Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs.

OBJECTIVE: This investigation aimed to verify and compare the performance of 3 deep learning systems for classifying maxillary impacted supernumerary teeth (ISTs) in patients with fully erupted incisors. STUDY DESIGN: In total, the study included 550 panoramic radiographs obtained from 275 patients with at least 1 IST and 275 patients without ISTs in the maxillary incisor region. Three learning models were created by using AlexNet, VGG-16, and DetectNet. Four hundred images were randomly selected as training data, and 100 images were assigned as validating and testing data. The remaining 50 images were used as new testing data. The sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve were calculated. Detection performance was evaluated by using recall, precision, and F-measure. RESULTS: DetectNet generally produced the highest values of diagnostic efficacy. VGG-16 yielded significantly lower values compared with DetectNet and AlexNet. Assessment of the detection performance of DetectNet showed that recall, precision, and F-measure for detection in the incisor region were all 1.0, indicating perfect detection. CONCLUSIONS: DetectNet and AlexNet appear to have potential use in classifying the presence of ISTs in the maxillary incisor region on panoramic radiographs. Additionally, DetectNet would be suitable for automatic detection of this abnormality.

Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs.

OBJECTIVE: The aim of this study was to compare time and storage space requirements, diagnostic performance, and consistency among 3 image recognition convolutional neural networks (CNNs) in the evaluation of the relationships between the mandibular third molar and the mandibular canal on panoramic radiographs. STUDY DESIGN: Of 600 panoramic radiographs, 300 each were assigned to noncontact and contact groups based on the relationship between the mandibular third molar and the mandibular canal. The CNNs were trained twice by using cropped image patches with sizes of 70 × 70 pixels and 140 × 140 pixels. Time and storage space were measured for each system. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were determined. Intra-CNN and inter-CNN consistency values were calculated. RESULTS: Time and storage space requirements depended on the depth of CNN layers and number of learned parameters, respectively. The highest AUC values ranged from 0.88 to 0.93 in the CNNs created by 70 × 70 pixel patches, but there were no significant differences in diagnostic performance among any of the models with smaller patches. Intra-CNN and inter-CNN consistency values were good or very good for all CNNs. CONCLUSIONS: The size of the image patches should be carefully determined to ensure acquisition of high diagnostic performance and consistency.

Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography.

OBJECTIVES: The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. METHODS: Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure. RESULTS: Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83. CONCLUSIONS: The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.

A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.

OBJECTIVES:: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. METHODS:: CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. RESULTS:: Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. CONCLUSIONS:: The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.

Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique.

OBJECTIVE: The aim of this study was to investigate whether a deep learning object detection technique can automatically detect and classify radiolucent lesions in the mandible on panoramic radiographs. STUDY DESIGN: Panoramic radiographs of patients with mandibular radiolucent lesions of 10 mm or greater, including ameloblastomas, odontogenic keratocysts, dentigerous cysts, radicular cysts, and simple bone cysts, were included. Lesion labels, including region of interest coordinates, were created in text format. In total, 210 training images and labels were imported into the deep learning GPU training system (DIGITS). A learning model was created using the deep neural network DetectNet. Two testing data sets (testing 1 and 2) were applied to the learning model. Similarities and differences between the prediction and ground-truth images were evaluated using Intersection over Union (IoU). Sensitivity and false-positive rate per image were calculated using an IoU threshold of 0.6. The detection performance for each disease was assessed using multiclass learning. RESULTS: Sensitivity was 0.88 for both testing 1 and 2. The false-positive rate per image was 0.00 for testing 1 and 0.04 for testing 2. The best combination of detection and classification sensitivity occurred with dentigerous cysts. CONCLUSIONS: Radiolucent lesions of the mandible can be detected with high sensitivity using deep learning.

Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.

OBJECTIVES: To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance. METHODS: Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents. RESULTS: The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents. CONCLUSIONS: The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.

Depression of the maxillary sinus anterior wall and its influence on panoramic radiography appearance.

OBJECTIVES: To clarify the depression aspect of the maxillary sinus anterior wall and to investigate its relationship with the panoramic image appearance of a diagonal line from the inferior part of the so-called panoramic innominate line to the medial portion of the orbital floor line. METHODS: Based on CT data, panoramic images were simulated for two typical cases with and without anterior wall depression. Next, on axial CT images of 1689 subjects (3378 sinuses) stored in our image database, the wall depths were measured and analyzed for their relationships with the panoramic appearances of the diagonal line, classified into invisible, obscure and clear patterns. RESULTS: Based on the simulation study, visualization of the diagonal line was verified to alter depending on the morphology of the anterior wall and the position of the panoramic image layer. In 408 (12.1%) sinuses, the diagonal line (clear and obscure patterns) could be seen on the panoramic image. The incidences of the obscure and clear patterns increased with increasing age groups. The mean wall depths were 2.91, 4.80 and 7.28 mm for the invisible, obscure and clear patterns, respectively. The clear pattern showed the highest value for the wall depth, followed by the obscure pattern. CONCLUSIONS: The diagonal line on a panoramic image was verified to be related to depression of the maxillary sinus anterior wall, and its panoramic image appearance can be altered depending on the position of the tomographic image layer.