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.

A cycle generative adversarial network for generating synthetic contrast-enhanced computed tomographic images from non-contrast images in the internal jugular lymph node-bearing area

The objectives of this study were to create a mutual conversion system between contrast-enhanced computed tomography (CECT) and non-CECT images using a cycle generative adversarial network (cycleGAN) for the internal jugular region. Image patches were cropped from CT images in 25 patients who underwent both CECT and non-CECT imaging. Using a cycleGAN, synthetic CECT and non-CECT images were generated from original non-CECT and CECT images, respectively. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were calculated. Visual Turing tests were used to determine whether oral and maxillofacial radiologists could tell the difference between synthetic versus original images, and receiver operating characteristic (ROC) analyses were used to assess the radiologists' performances in discriminating lymph nodes from blood vessels. The PSNR of non-CECT images was higher than that of CECT images, while the SSIM was higher in CECT images. The Visual Turing test showed a higher perceptual quality in CECT images. The area under the ROC curve showed almost perfect performances in synthetic as well as original CECT images. In conclusion, synthetic CECT images created by cycleGAN appeared to have the potential to provide effective information in patients who could not receive contrast enhancement.

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.

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.

Differences in the panoramic appearance of cleft alveolus patients with or without a cleft palate

Purpose

The purpose of this study was to clarify the panoramic image differences of cleft alveolus patients with or without a cleft palate, with emphases on the visibility of the line formed by the junction between the nasal septum and nasal floor (the upper line) and the appearances of the maxillary lateral incisor.

Materials and Methods

Panoramic radiographs of 238 patients with cleft alveolus were analyzed for the visibility of the upper line, including clear, obscure or invisible, and the appearances of the maxillary lateral incisor, regarding congenital absence, incomplete growth, delayed eruption and medial inclination. Differences in the distribution ratio of these visibility and appearances were verified between the patients with and without a cleft palate using the chi-square test.

Results

There was a significant difference in the visibility distribution of the upper line between the patients with and without a cleft palate (p<0.05). In most of the patients with a cleft palate, the upper line was not observed. In the unilateral cleft alveolus patients, the medial inclination of the maxillary lateral incisor was more frequently observed in patients with a cleft palate than in patients without a cleft palate.

Conclusion

Two differences were identified in panoramic appearances. The first was the disappearance (invisible appearance) of the upper line in patients with a cleft palate, and the second was a change in the medial inclination on the affected side maxillary lateral incisor in unilateral cleft alveolus patients with a cleft palate.

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.

Does ultrasound elastography have a role as a diagnostic method for Sjögren's syndrome in the salivary glands? A systematic review

OBJECTIVE

This systematic review was performed to examine the usefulness of salivary gland ultrasound elastography (USE) as a diagnostic tool for Sjögren's syndrome (SjS).

METHODS

Electronic databases (MEDLINE, EMBASE, the Cochrane Library, and Web of Science: Science Citation Index) were searched to identify studies using USE to diagnose SjS from database inception to 15 July 2022. The primary outcome was improved diagnostic accuracy for SjS with the use of USE. Risk of bias and applicability concerns were assessed using the GRADE system, which is continuously developed by the GRADE Working Group.

RESULTS

Among 4550 screened studies, 24 full-text articles describing the applications of USE to diagnose SjS were reviewed. The overall risk of bias was determined to be low for 17 of the 24 articles, medium for 5, and high for 2. Articles comparing patients with SjS and healthy subjects reported high diagnostic accuracy of USE, with most results showed statistically significant differences (parotid glands: 15 of the 16 articles, submandibular glands: 11 of the 14 articles).

CONCLUSIONS

This systematic review suggests that the assessment of salivary glands using USE is a useful diagnostic tool for SjS.

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.

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.

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.

Alterations of posterior pharyngeal wall movement during swallowing in postoperative tongue cancer patients: assessment with a videofluoroscopic swallowing study

This study aimed to determine the association between the progressive contraction of the posterior pharyngeal wall and dysphagia in postoperative patients with tongue cancer. A videofluoroscopic swallowing study (VFSS) was performed in 34 patients after tongue cancer surgery. Images were analyzed using a two-dimensional video measurement software. Cases in which the processes on the posterior pharyngeal wall moved downward from the 2nd to 4th vertebral regions were defined as "normal type", other cases were defined as "abnormal type". Twenty-four patients showed normal movement of the posterior pharyngeal wall, whereas 10 patients showed the abnormal type. The results showed that there was a significant difference in dysphagia scores between the postoperative swallowing type and swallowing dysfunction score. This implies that dysphagia is related to the movement of the posterior pharyngeal wall after tongue cancer surgery. Furthermore, the extent of resection and stage were significantly different between the normal and abnormal groups in the posterior pharyngeal wall movement. There was also a significant difference between the two groups in terms of the following: whether the tongue base was included in the excision range (p < 0.01), whether neck dissection was performed (p < 0.01), or whether reconstruction was not performed (p < 0.01). VFSS results showed that posterior pharyngeal wall movement was altered after surgery in patients with tongue cancer who had severe dysphagia.

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.

A preliminary deep learning study on automatic segmentation of contrast-enhanced bolus in videofluorography of swallowing

Although videofluorography (VFG) is an effective tool for evaluating swallowing functions, its accurate evaluation requires considerable time and effort. This study aimed to create a deep learning model for automated bolus segmentation on VFG images of patients with healthy swallowing and dysphagia using the artificial intelligence deep learning segmentation method, and to assess the performance of the method. VFG images of 72 swallowing of 12 patients were continuously converted into 15 static images per second. In total, 3910 images were arbitrarily assigned to the training, validation, test 1, and test 2 datasets. In the training and validation datasets, images of colored bolus areas were prepared, along with original images. Using a U-Net neural network, a trained model was created after 500 epochs of training. The test datasets were applied to the trained model, and the performances of automatic segmentation (Jaccard index, Sørensen-Dice coefficient, and sensitivity) were calculated. All performance values for the segmentation of the test 1 and 2 datasets were high, exceeding 0.9. Using an artificial intelligence deep learning segmentation method, we automatically segmented the bolus areas on VFG images; our method exhibited high performance. This model also allowed assessment of aspiration and laryngeal invasion.

Multiple assessment of molars with hypercementosis lost due to periodontitis using X-ray micro-computed tomography, electron microprobe analysis, and histological sections

This article aimed to achieve a better understanding of cementum hyperplasia in the maxillary second molars lost due to periodontitis. Six maxillary second molars with hypercementosis were measured for the mineral concentration using micro-computed tomography and calcium element distributions using electron microprobe analysis. Calcium was distributed throughout the cementum, although the mineral concentration differed based on the cementum depth. The hyperplastic cementum was of the extrinsic fiber-rich cellular mixed stratified type. These results have implications for future studies aiming to diagnose hypercementosis. Further studies are needed to investigate the composition of the cementum matrix.

Segmentation of metastatic cervical lymph nodes from CT images of oral cancers using deep learning technology

OBJECTIVE

The purpose of this study was to establish a deep learning model for segmenting the cervical lymph nodes of oral cancer patients and diagnosing metastatic or non-metastatic lymph nodes from contrast-enhanced computed tomography (CT) images.

METHODS

CT images of 158 metastatic and 514 non-metastatic lymph nodes were prepared. CT images were assigned to training, validation, and test datasets. The colored images with lymph nodes were prepared together with the original images for the training and validation datasets. Learning was performed for 200 epochs using the neural network U-net. Performance in segmenting lymph nodes and diagnosing metastasis were obtained.

RESULTS

Performance in segmenting metastatic lymph nodes showed recall of 0.742, precision of 0.942, and F1 score of 0.831. The recall of metastatic lymph nodes at level II was 0.875, which was the highest value. The diagnostic performance of identifying metastasis showed an area under the curve (AUC) of 0.950, which was significantly higher than that of radiologists (0.896).

CONCLUSIONS

A deep learning model was created to automatically segment the cervical lymph nodes of oral squamous cell carcinomas. Segmentation performances should still be improved, but the segmented lymph nodes were more accurately diagnosed for metastases compared with evaluation by humans.

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.

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.

Orthodontic tooth movement-activated sensory neurons contribute to enhancing osteoclast activity and tooth movement through sympathetic nervous signalling

OBJECTIVES

Orthodontic tooth movement (OTM) increases sympathetic and sensory neurological markers in periodontal tissue. However, the relationship between the sympathetic and sensory nervous systems during OTM remains unclear. Therefore, the present study investigated the relationship between the sympathetic and sensory nervous systems activated by OTM using pharmacological methods.

MATERIALS AND METHODS

We compared the effects of sympathectomy and sensory nerve injury during OTM in C57BL6/J mice. Capsaicin (CAP) was used to induce sensory nerve injury. Sympathectomy was performed using 6-hydroxydopamine. To investigate the effects of a β-agonist on sensory nerve injury, isoproterenol (ISO) was administered to CAP-treated mice. Furthermore, to examine the role of the central nervous system in OTM, the ventromedial hypothalamic nucleus (VMH) was ablated using gold thioglucose.

RESULTS

Sensory nerve injury and sympathectomy both suppressed OTM and decreased the percent of the alveolar socket covered with osteoclasts (Oc.S/AS) in periodontal tissue. Sensory nerve injury inhibited increases in OTM-induced calcitonin gene-related peptide (CGRP) immunoreactivity (IR), a marker of sensory neurons, and tyrosine hydroxylase (TH) IR, a marker of sympathetic neurons, in periodontal tissue. Although sympathectomy did not decrease the number of CGRP-IR neurons in periodontal tissue, OTM-induced increases in the number of TH-IR neurons were suppressed. The ISO treatment restored sensory nerve injury-inhibited tooth movement and Oc.S/AS. Furthermore, the ablation of VMH, the centre of the sympathetic nervous system, suppressed OTM-induced increases in tooth movement and Oc.S/AS.

CONCLUSIONS

The present results suggest that OTM-activated sensory neurons contribute to enhancements in osteoclast activity and tooth movement through sympathetic nervous signalling.

Efficacy of a deep leaning model created with the transfer learning method in detecting sialoliths of the submandibular gland on panoramic radiography

OBJECTIVE

This study aimed to compare the performance of 3 deep learning models, including a model constructed with the transfer learning method, in detecting submandibular gland sialoliths on panoramic radiographs.

STUDY DESIGN

We used data from 2 institutions (A and B) to create the models for use in institution B. In total, 224 panoramic radiographs with sialoliths were used. Model 1 was created using data from institution A only, model 2 was created using combined data from institutions A and B, and model 3 was created using the transfer learning method by having model 1 transferred and trained in various learning epochs using data from institution B. These models were tested and compared in their detection performance using testing data sets from institution B.

RESULTS

Model 2 and model 3 with 300 epochs performed equally well and yielded the highest detection rates (recall: sensitivity of 85%, precision: positive predictive value of 100%, and F measure of 91.9%) for sialoliths on panoramic radiographs.

CONCLUSION

The results of this study suggest that use of the transfer learning method with an appropriate number of epochs may be an alternative to sharing patient personal data among institutions.

Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine

Objectives

The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques.

Methods

We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems.

Results

The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927).

Conclusions

The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.

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.

General rules for clinical and pathological studies on oral cancer (2nd edition): a synopsis

For doctors and other medical staff treating oral cancer, it is necessary to standardize the basic concepts and rules for oral cancer to achieve progress in its treatment, research, and diagnosis. Oral cancer is an integral part of head and neck cancer and is treated in accordance with the general rules for head and neck cancer. However, detailed rules based on the specific characteristics of oral cancer are essential. The objective of this article was to contribute to the development of the diagnosis, treatment, and research of oral cancer, based on the correct and useful medical information of clinical, surgical, pathological, and imaging findings accumulated from individual patients at various institutions. Our general rules were revised as the UICC was revised for the 8th edition and were published as the Japanese second edition in 2019. In this paper, the English edition of the "Rules" section is primarily presented.

Clinical assessment of cemento-osseous dysplasia based on three-dimensional diagnostic imaging: A case report

Cemento-osseous dysplasia (COD) is a lesion in which periapical bone is replaced by fibrous tissue, including osseous or cementum-like tissue. In the initial stage of COD, radiolucencies are noted at the root apex on periapical radiography, which can be confused with apical periodontitis. Understanding of correct pathological condition and careful assessment of COD is critical to avoid unnecessary endodontic interventions in healthy teeth. This report describes the ability and usefulness of cone-beam computed tomography (CBCT) and multi-slice computed tomography (MSCT) to detect COD. The findings in this case suggest that MSCT is more appropriate than CBCT, especially for patients with early- to middle-stage COD. However, the radiation dose is higher in MSCT than in CBCT; the application of MSCT should be limited to assessment of whether treatment or surgical management is necessary.

Effects of 1 year of training on the performance of ultrasonographic image interpretation: A preliminary evaluation using images of Sjögren syndrome patients

Purpose

This study investigated the effects of 1 year of training on imaging diagnosis, using static ultrasonography (US) salivary gland images of Sjögren syndrome patients.

Materials and Methods

This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases. The 3 radiologists (observers A, B, and C) evaluated 400 static US images of salivary glands twice at a 1-year interval. To compare their performance, 2 experienced radiologists evaluated the same images. Diagnostic performance was compared between the 2 evaluations using the area under the receiver operating characteristic curve (AUC).

Results

Observer A, who was participating in the training program for the second year, exhibited no significant difference in AUC between the first and second evaluations, with results consistently comparable to those of experienced radiologists. After 1 year of training, observer B showed significantly higher AUCs than before training. The diagnostic performance of observer B reached the level of experienced radiologists for parotid gland assessment, but differed for submandibular gland assessment. For observer C, who did not complete the training, there was no significant difference in the AUC between the first and second evaluations, both of which showed significant differences from those of the experienced radiologists.

Conclusion

These preliminary results suggest that the training program effectively helped inexperienced radiologists reach the level of experienced radiologists for US examinations.

Application of Deep Learning in the Identification of Cerebral Hemodynamics Data Obtained from Functional Near-Infrared Spectroscopy: A Preliminary Study of Pre- and Post-Tooth Clenching Assessment

In fields using functional near-infrared spectroscopy (fNIRS), there is a need for an easy-to-understand method that allows visual presentation and rapid analysis of data and test results. This preliminary study examined whether deep learning (DL) could be applied to the analysis of fNIRS-derived brain activity data. To create a visual presentation of the data, an imaging program was developed for the analysis of hemoglobin (Hb) data from the prefrontal cortex in healthy volunteers, obtained by fNIRS before and after tooth clenching. Three types of imaging data were prepared: oxygenated hemoglobin (oxy-Hb) data, deoxygenated hemoglobin (deoxy-Hb) data, and mixed data (using both oxy-Hb and deoxy-Hb data). To differentiate between rest and tooth clenching, a cross-validation test using the image data for DL and a convolutional neural network was performed. The network identification rate using Hb imaging data was relatively high (80‒90%). These results demonstrated that a method using DL for the assessment of fNIRS imaging data may provide a useful analysis system.

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%.

Automatic measurement of mandibular cortical bone width on cone-beam computed tomography images

OBJECTIVE

The computed tomography cortical index (CTCI), computed tomography mandibular index (CTMI), and computed tomography index (inferior) [CTI(I)] are indexes obtained from cone-beam computed tomography images for the assessment of the mandibular cortex quality for implant planning or osteoporosis. However, cross-sectional image reconstruction for the measurements is labor-intensive. This study aimed to develop and evaluate a method to automatically reconstruct cross-sectional images and measure the cortex width in all areas inferior to the mental foramen (MF).

METHODS

Seventy-one women (mean age: 52.4 years; range: 20-78 years) were enrolled. They were divided into four age and CTCI groups, including females younger (FY) and females older (FO) than 50 years (C1: normal, C2: mild/moderate erosion, and C3: severe porosity). Automatic and manual measurements of CTMI and CTI(I) were compared, and the inter- and intraobserver agreements were assessed using the intraclass correlation coefficient (ICC). The relationships between CTMI or CTI(I) and CTCI were also assessed.

RESULTS

The mean processing times for reconstruction and measurements were 31.9 s and 1.22 s, respectively. ICCs for the comparison of automatic and manual measurements were 0.932 and 0.993 in the C1 and C2/C3 groups, respectively. Significant differences in CTMI and CTI(I) were observed between the FY or the FO-C1 and FO-C3 groups (p < 0.05).

CONCLUSION

The automatic and manual measurements showed a strong agreement. The new method could drastically reduce routine clinical workload. Additionally, our method enables the measurement of the cortex width in all the mandibular bones inferior to the MF.

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.

Computed tomographic features of synovial chondromatosis of the temporomandibular joint with a few small calcified loose bodies

OBJECTIVES

The present study aimed to clarify the characteristic computed tomography (CT) features that indicate synovial chondromatosis (SC) with a few small calcified bodies or without calcification on panoramic images, and to discuss their differences from the features of temporomandibular disorder (TMD).

METHODS

Panoramic and CT images from 11 patients with histologically verified SC of the temporomandibular joint were investigated. Based on the panoramic images, the patients were classified into a distinct group (5 patients) with typical features of calcified loose bodies and an indistinct group (6 patients) without such bodies. On the CT images, findings for high-density structures suggesting calcified loose bodies, joint space widening, and bony changes in the articular eminence and glenoid fossa (eminence/fossa) and condyle were analyzed.

RESULTS

All 5 distinct group patients showed high-density structures on CT images, while 2 of 6 indistinct group patients showed no high-density structures even on soft-tissue window CT images. A significant difference was found for the joint space distance between the affected and unaffected sides. A low-density area relative to the surrounding muscles, suggesting joint space widening, was observed on the affected side in 2 indistinct group patients. All 11 patients regardless of distinct or indistinct classification showed bony changes in the eminence/fossa with predominant findings of extended sclerosis and erosion.

CONCLUSION

Eminence/fossa osseous changes including extended sclerosis and erosion may be effective CT features for differentiating SC from TMD even when calcified loose bodies cannot be identified.

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.

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.

Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images

OBJECTIVES

This study estimated the diagnostic performance of a deep learning system for detection of Sjögren's syndrome (SjS) on CT, and compared it with the performance of radiologists.

METHODS

CT images were assessed from 25 patients confirmed to have SjS based on the both Japanese criteria and American-European Consensus Group criteria and 25 control subjects with no parotid gland abnormalities who were examined for other diseases. 10 CT slices were obtained for each patient. From among the total of 500 CT images, 400 images (200 from 20 SjS patients and 200 from 20 control subjects) were employed as the training data set and 100 images (50 from 5 SjS patients and 50 from 5 control subjects) were used as the test data set. The performance of a deep learning system for diagnosing SjS from the CT images was compared with the diagnoses made by six radiologists (three experienced and three inexperienced radiologists).

RESULTS

The accuracy, sensitivity, and specificity of the deep learning system were 96.0%, 100% and 92.0%, respectively. The corresponding values of experienced radiologists were 98.3%, 99.3% and 97.3% being equivalent to the deep learning, while those of inexperienced radiologists were 83.5%, 77.9% and 89.2%. The area under the curve of inexperienced radiologists were significantly different from those of the deep learning system and the experienced radiologists.

CONCLUSIONS

The deep learning system showed a high diagnostic performance for SjS, suggesting that it could possibly be used for diagnostic support when interpreting CT images.

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.

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.

Magnetic resonance imaging in endodontics: a literature review

OBJECTIVES

Magnetic resonance imaging (MRI) has recently been used for the evaluation of dental pulp anatomy, vitality, and regeneration. This study reviewed the recent use of MRI in the endodontic field.

METHODS

Literature published from January 2000 to March 2017 was searched in PubMed using the following Medical Subject Heading (MeSH) terms: (1) MRI and (dental pulp anatomy or endodontic pulp); (2) MRI and dental pulp regeneration. Studies were narrowed down based on specific inclusion criteria and categorized as in vitro, in vivo, or dental pulp regeneration studies. The MRI sequences and imaging findings were summarized.

RESULTS

In the in vitro studies on dental pulp anatomy, T1-weighted imaging with high resolution was frequently used to evaluate dental pulp morphology, demineralization depth, and tooth abnormalities. Other sequences such as apparent diffusion coefficient mapping and sweep imaging with Fourier transformation were used to evaluate pulpal fluid and decayed teeth, and short-T2 tissues (dentin and enamel), respectively. In the in vivo studies, pulp vitality and reperfusion were visible with fat-saturated T2-weighted imaging or contrast-enhanced T1-weighted imaging. In both the in vitro and in vivo studies, MRI could reveal pulp regeneration after stem cell therapy. Stem cells labeled with superparamagnetic iron oxide particles were also visible on MRI. Angiogenesis induced by stem cells could be confirmed on enhanced T1-weighted imaging.

CONCLUSION

MRI can be successfully used to visualize pulp morphology as well as pulp vitality and regeneration. The use of MRI in the endodontic field is likely to increase in the future.

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.

Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence

OBJECTIVE

Although the deep learning system has been applied to interpretation of medical images, its application to the diagnosis of cervical lymph nodes in patients with oral cancer has not yet been reported. The purpose of this study was to evaluate the performance of deep learning image classification for diagnosis of lymph node metastasis.

STUDY DESIGN

The imaging data used for evaluation consisted of computed tomography (CT) images of 127 histologically proven positive cervical lymph nodes and 314 histologically proven negative lymph nodes from 45 patients with oral squamous cell carcinoma. The performance of a deep learning image classification system for the diagnosis of lymph node metastasis on CT images was compared with the diagnostic interpretations of 2 experienced radiologists by using the Mann-Whitney U test and χ² analysis.

RESULTS

The performance of the deep learning image classification system resulted in accuracy of 78.2%, sensitivity of 75.4%, specificity of 81.0%, positive predictive value of 79.9%, negative predictive value of 77.1%, and area under the receiver operating characteristic curve of 0.80. These values were not significantly different from those found by the radiologists.

CONCLUSIONS

The deep learning system yielded diagnostic results similar to those of the radiologists, which suggests that this system may be valuable for diagnostic support.

Longitudinal observation of maxillary sinus bony bridges and septa in childhood

The rate of septum presence in the maxillary sinus has been reported to be over 30%. It was considered that a bony bridge might change to a maxillary sinus septum with growth in a previous study using dry child skulls. In the present investigation, maxillary sinus bony bridges and septa were longitudinally observed using computed tomography (CT). Multislice CT was performed in three patients. A bony bridge was defined as a bony structure between the maxillary sinus wall and dental germ. Also, a septum was defined as a pointed bony structure in the inferior wall of the maxillary sinus. The height and angle of the bony bridge/septum and the distance between the base of the bony bridge/septum and bony palate were measured. In three patients, the bony bridge in the maxillary sinus floor was observed in the second molar on the first CT, and the maxillary sinus septum was observed on the second CT at the same site. In conclusion, it was longitudinally observed that a bony bridge changed to a maxillary sinus septum with growth, such as root formation and tooth eruption.

Quantification of voxel values in micro computed tomography using multiple porosity hydroxyapatite blocks

Microfocus X-ray computed tomography (micro-CT) has been applied as a method for the nondestructive and detailed assessment of trabecular bone patterns and tooth structure. Voxel values obtained from micro-CT are not absolute values. Therefore, voxel values were assessed using hydroxyapatite (HA) blocks with a different vesicle rate to quantify voxel values of micro-CT images in the present investigation.HA blocks with 4 levels of porosity and a block with a soft tissue-equivalent density were used, and the voxel values of each block were measured. Correlations between voxel values of micro-CT and HA densities were analyzed. Also, black and white binary images were produced, and the ratios of white pixels to pixels in regions of interest (ROIs) were calculated. The relationship between voxel values of micro-CT and HA densities could be regressed using a linear equation, and the correlation coefficient was high. Also, there were no significant differences in the regression equations between the first and second times. Voxel values of micro-CT might be convertible to HA densities using a regression equation.

Muscle hardness and masticatory myofascial pain: Assessment and clinical relevance

The impression of increased muscle hardness in painful muscles is commonly reported in the clinical practice but may be difficult to assess. Therefore, the aim of this review was to present and discuss relevant aspects regarding the assessment of muscle hardness and its association with myofascial temporomandibular disorder (TMD) pain. A non-systematic search for studies of muscle hardness assessment in patients with pain-related TMDs was carried out in PubMed, Cochrane Library, Embase and Google Scholar. Mechanical devices and ultrasound imaging (strain and shear wave elastography) have been consistently used to measure masticatory muscle hardness, although an undisputable reference standard is yet to be determined. Strain elastography has identified greater masseter hardness of the symptomatic side in patients with unilateral myofascial TMD pain when compared to the contralateral side and healthy controls (HC). Likewise, shear wave elastography has shown greater masseter elasticity modulus in patients with myofascial TMD pain when compared to HC, which may be an indication of muscle hardness. Although assessment bias could partly explain these preliminary findings, future randomised controlled trials are encouraged to investigate this relationship. This qualitative review indicates that the muscle hardness of masticatory muscles is still a rather unexplored field of investigation with a good potential to improve the assessment and potentially also the management of myofascial TMD pain. Nonetheless, the current evidence in favour of increased hardness in masticatory muscles in patients with myofascial TMD pain is weak, and the pathophysiological importance and clinical usefulness of such information remain unclear.

Optimizing the reconstruction filter in cone-beam CT to improve periodontal ligament space visualization: An in vitro study

Purpose

Evaluation of alveolar bone is important in the diagnosis of dental diseases. The periodontal ligament space is difficult to clearly depict in cone-beam computed tomography images because the reconstruction filter conditions during image processing cause image blurring, resulting in decreased spatial resolution. We examined different reconstruction filters to assess their ability to improve spatial resolution and allow for a clearer visualization of the periodontal ligament space.

Materials and Methods

Cone-beam computed tomography projections of 2 skull phantoms were reconstructed using 6 reconstruction conditions and then compared using the Thurstone paired comparison method. Physical evaluations, including the modulation transfer function and the Wiener spectrum, as well as an assessment of space visibility, were undertaken using experimental phantoms.

Results

Image reconstruction using a modified Shepp-Logan filter resulted in better sensory, physical, and quantitative evaluations. The reconstruction conditions substantially improved the spatial resolution and visualization of the periodontal ligament space. The difference in sensitivity was obtained by altering the reconstruction filter.

Conclusion

Modifying the characteristics of a reconstruction filter can generate significant improvement in assessments of the periodontal ligament space. A high-frequency enhancement filter improves the visualization of thin structures and will be useful when accurate assessment of the periodontal ligament space is necessary.

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.

Computed Tomographic Estimation of Particulate Cancellous Bone and Marrow Weight for Successful Transplant in Unilateral Cleft Lip and Palate Patients

Objective

The defect volume measured on computed tomography (CT) for secondary bone graft (SBG) is well correlated to the actual amount of particulate cancellous bone and marrow (PCBM) transplanted in unilateral cleft lip and palate (UCLP) patients. However, the validity of such measurements have not been completely verified due to lack of evaluation of treatment results. The objective of this study was to propose an estimation method by CT based on the data of successfully treated patients. For this purpose, the association was initially verified between the weight of transplanted PCBM and the defect volume measured on CT using the results of successfully treated patients.

Methods

Treatment results were evaluated 1 year after SBG by intraoral radiography in 50 UCLP patients. For the patients with good results, the correlation was investigated between the defect volume on CT and the transplanted PCBM weight, and a method was proposed based on PCBM density, calculated as PCBM weight divided by defect volume on CT.

Results

In successfully treated patients showing level 3 or 4 alveolar resorption, a strong correlation ( r = .87) was found between the volume on CT and the PCBM weight. Level 4 results were observed in 22 of 23 (95.7%) patients who had calculated PCBM densities of more than 6 g/cm3.

Conclusions

Volume estimation on preoperative CT was confirmed to have sufficient validity. The weight of PCBM transplanted should be greater than the defect volume on CT multiplied by 6.