Generative adversarial networks in dental imaging: a systematic review.

OBJECTIVES: This systematic review on generative adversarial network (GAN) architectures for dental image analysis provides a comprehensive overview to readers regarding current GAN trends in dental imagery and potential future applications. METHODS: Electronic databases (PubMed/MEDLINE, Scopus, Embase, and Cochrane Library) were searched to identify studies involving GANs for dental image analysis. Eighteen full-text articles describing the applications of GANs in dental imagery were reviewed. Risk of bias and applicability concerns were assessed using the QUADAS-2 tool. RESULTS: GANs were used for various imaging modalities, including two-dimensional and three-dimensional images. In dental imaging, GANs were utilized for tasks such as artifact reduction, denoising, and super-resolution, domain transfer, image generation for augmentation, outcome prediction, and identification. The generated images were incorporated into tasks such as landmark detection, object detection and classification. Because of heterogeneity among the studies, a meta-analysis could not be conducted. Most studies (72%) had a low risk of bias in all four domains. However, only three (17%) studies had a low risk of applicability concerns. CONCLUSIONS: This extensive analysis of GANs in dental imaging highlighted their broad application potential within the dental field. Future studies should address limitations related to the stability, repeatability, and overall interpretability of GAN architectures. By overcoming these challenges, the applicability of GANs in dentistry can be enhanced, ultimately benefiting the dental field in its use of GANs and artificial intelligence.

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.

Unusual imaging appearance of cemental tear in the maxillary first molar on cone-beam computed tomography: A case report.

A cemental tear (CeT) is a definitive clinical entity and its radiographic appearance is well known in single-rooted teeth. However, the imaging features of CeT in multi-rooted teeth have not been clarified. We report a case of CeT which arose in the maxillary first molar and exhibited an unusual appearance in cone-beam computed tomography images. The torn structure was verified as cementum by micro-computed tomography and histological analysis. The hypercementosis, most likely induced by occlusal force, might have been torn from the root by a stronger occlusal force caused by the mandibular implant. An unusual bridging structure was created between the two buccal roots. These features may occur in multi-rooted teeth with long-standing deep pockets and abscesses that are resistant to treatment.

External Validation of the Effect of the Combined Use of Object Detection for the Classification of the C-Shaped Canal Configuration of the Mandibular Second Molar in Panoramic Radiographs: A Multicenter Study.

INTRODUCTION: The purposes of this study were to evaluate the effect of the combined use of object detection for the classification of the C-shaped canal anatomy of the mandibular second molar in panoramic radiographs and to perform an external validation on a multicenter dataset. METHODS: The panoramic radiographs of 805 patients were collected from 4 institutes across two countries. The CBCT data of the same patients were used as "Ground-truth". Five datasets were generated: one for training and validation, and 4 as external validation datasets. Workflow 1 used manual cropping to prepare the image patches of mandibular second molars, and then classification was performed using EfficientNet. Workflow 2 used two combined methods with a preceding object detection (YOLOv7) performed for automated image patch formation, followed by classification using EfficientNet. Workflow 3 directly classified the root canal anatomy from the panoramic radiographs using the YOLOv7 prediction outcomes. The classification performance of the 3 workflows was evaluated and compared across 4 external validation datasets. RESULTS: For Workflows 1, 2, and 3, the area under the receiver operating characteristic curve (AUC) values were 0.863, 0.861, and 0.876, respectively, for the AGU dataset; 0.935, 0.945, and 0.863, respectively, for the ASU dataset; 0.854, 0.857, and 0.849, respectively, for the ODU dataset; and 0.821, 0.797, and 0.831, respectively, for the ODU low-resolution dataset. No significant differences existed between the AUC values of Workflows 1, 2, and 3 across the 4 datasets. CONCLUSIONS: The deep learning systems of the 3 workflows achieved significant accuracy in predicting the C-shaped canal in mandibular second molars across all test datasets.

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.

Evaluating the performance of generative adversarial network-synthesized periapical images in classifying C-shaped root canals.

This study evaluated the performance of generative adversarial network (GAN)-synthesized periapical images for classifying C-shaped root canals, which are challenging to diagnose because of their complex morphology. GANs have emerged as a promising technique for generating realistic images, offering a potential solution for data augmentation in scenarios with limited training datasets. Periapical images were synthesized using the StyleGAN2-ADA framework, and their quality was evaluated based on the average Frechet inception distance (FID) and the visual Turing test. The average FID was found to be 35.353 (± 4.386) for synthesized C-shaped canal images and 25.471 (± 2.779) for non C-shaped canal images. The visual Turing test conducted by two radiologists on 100 randomly selected images revealed that distinguishing between real and synthetic images was difficult. These results indicate that GAN-synthesized images exhibit satisfactory visual quality. The classification performance of the neural network, when augmented with GAN data, showed improvements compared with using real data alone, and could be advantageous in addressing data conditions with class imbalance. GAN-generated images have proven to be an effective data augmentation method, addressing the limitations of limited training data and computational resources in diagnosing dental anomalies.

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.

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.

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.

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.

Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images.

OBJECTIVES: We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists. METHODS: 100 patients with a confirmed diagnosis of SjS according to both the Japanese criteria and American-European Consensus Group criteria and 100 non-SjS patients that had a dry mouth and suspected SjS but were definitively diagnosed as non-SjS were enrolled in this study. All the patients underwent US scans of both the parotid glands (PG) and submandibular glands (SMG). The training group consisted of 80 SjS patients and 80 non-SjS patients, whereas the test group consisted of 20 SjS patients and 20 non-SjS patients for deep learning analysis. The performance of the deep learning system for diagnosing SjS from the US images was compared with the diagnoses made by three inexperienced radiologists. RESULTS: The accuracy, sensitivity and specificity of the deep learning system for the PG were 89.5, 90.0 and 89.0%, respectively, and those for the inexperienced radiologists were 76.7, 67.0 and 86.3%, respectively. The deep learning system results for the SMG were 84.0, 81.0 and 87.0%, respectively, and those for the inexperienced radiologists were 72.0, 78.0 and 66.0%, respectively. The AUC for the inexperienced radiologists was significantly different from that of the deep learning system. CONCLUSIONS: The deep learning system had a high diagnostic ability for SjS. This suggests that deep learning could be used for diagnostic support when interpreting US images.

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.

Use of tungsten sheet as an alternative for reducing the radiation dose behind the digital imaging plate during intra-oral radiography.

OBJECTIVES: To verify the use of tungsten sheet as an alternative to lead foil for reducing the radiation dose behind storage phosphor plates (SPPs). METHODS: At six sites (incisor, canine, and molar sites in both the maxilla and mandible) in a head phantom, radiation doses were initially measured behind conventional film packets containing two films and a lead foil. At the same sites, radiation doses were also measured behind packets containing only SPPs. Thereafter, the same dose measurements were performed with shielding materials (lead foil or tungsten sheet) within the packets. These doses were defined as behind doses. RESULTS: There were no differences in the mean behind doses between the conventional film packets and the SPP packets without shielding materials for any of the six sites examined. The behind doses were reduced by both lead foil and tungsten sheet, with significant differences in all sites when compared with no shielding. Lead foil reduced the behind dose of the SPP packet to 37.6% on average, while tungsten sheet reduced the behind dose to less than 20% in all of the sites examined, with an average of 14.7%. CONCLUSIONS: Tungsten sheet appeared to be effective as an alternative shielding material, sufficiently reducing the doses behind the SPP packets to less than 20% when compared with sheetless packets in all of the six sites examined.

Orthodontic tooth separation activates the hypothalamic area in the human brain.

OBJECTIVES: An animal experiment clarified that insertion of an orthodontic apparatus activated the trigeminal neurons of the medulla oblongata. Orthodontic tooth movement is known to be associated with the sympathetic nervous system and controlled by the nucleus of the hypothalamus. However, the transmission of both has not been demonstrated in humans. The purpose of this study were to examine the activated cerebral areas using brain functional magnetic resonance imaging (MRI), when orthodontic tooth separators were inserted, and to confirm the possibility of the transmission route from the medulla oblongata to the hypothalamus. METHODS: Two types of alternative orthodontic tooth separators (brass contact gauge and floss) were inserted into the right upper premolars of 10 healthy volunteers. Brain functional T2*-weighted images and anatomical T1-weighted images were taken. RESULTS: The blood oxygenation level dependent (BOLD) signals following insertion of a brass contact gauge and floss significantly increased in the somatosensory association cortex and hypothalamic area. CONCLUSION: Our findings suggest the possibility of a transmission route from the medulla oblongata to the hypothalamus.

Reproducibility of maxillofacial landmark identification on three-dimensional cone-beam computed tomography images of patients with mandibular prognathism: Comparative study of a tentative method and traditional cephalometric analysis.

OBJECTIVE: To clarify the reproducibility of a tentative method for identifying maxillofacial landmarks on three-dimensional (3D) images obtained with cone-beam computed tomography (CBCT) for dental use in patients with mandibular prognathism. Also, the influence of level of experience of dentists applying the method was investigated by dividing them into two groups according to experience. MATERIALS AND METHODS: Dentists with less (group A) or more (group B) than 3 years of experience of cephalometry and 3D image manipulation analyzed CBCT data from 10 patients using two different landmark identification methods: method 1 used conventional cephalometric definitions and method 2 used detailed landmark identification definitions developed for each cross-sectional plane. The plotting of nine landmarks was performed twice, and 10 coordinate values were obtained for each landmark. To assess reproducibility, the 95% confidence ellipse method was used. RESULTS: Comparative analysis showed that method 2 was highly reproducible. Group B subjects attained smaller ellipsoid volumes than group A subjects, regardless of the landmark identification method used. With method 1, except for condyle and coronoid process, all landmarks showed a higher level of reproducibility in group A subjects than in group B subjects. With method 2, however, five landmarks showed no differences between the methods. CONCLUSION: The method proposed here may be highly reproducible regardless of the evaluators' experience.