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To identify patients with nasopharyngeal cancer in whom the cervical radiation field can be reduced, we classified the growth patterns of nasopharyngeal cancer based on MRI findings into 4 types and performed an evaluation. Based on MRI findings, we classified the growth patterns of primary cancer in 94 patients with nasopharyngeal cancer into Type 1 (superficial type), Type 2 (lateral invasive type), Type 3 (upward invasive type), and Type 4 (anterior extension type), and further classified Type 2, based upon nasopharyngoscopic findings, into Type 2a (unilateral invasive type) and Type 2b (bilateral invasive type). The cervical lymph node metastasis areas were evaluated according to these types. Type 2 showed a significantly higher incidence of cervical lymph node metastasis only on the ipsilateral side than the other types (p = 0.0024). In particular, all patients with Type 2a had cervical lymph node metastasis only on the ipsilateral side (p = 0.0212). This study suggests that the distribution of metastasised cervical lymph nodes depends on the pattern of tumour extent of the primary site. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to determine the usefulness of a new MRI sequence, CT-like fast field echo with limited echo-spacing (FRACTURE), in diagnosing temporomandibular joint osteoarthritis compared with routine MRI temporomandibular joint (TMJ) sequences., Methods: The study sample comprised 76 patients (152 joints) who underwent MRI and CT examinations to diagnose TMJ disorders. Two specialists in oral and maxillofacial radiology assessed the bony changes of the TMJ on FRACTURE, proton density-weighted (PDw), and fat-suppression T2-weighted (T2wFS) sequences. Receiver operating characteristic curves were plotted for each sequence, and the accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. Additionally, the interobserver agreement (Cohen's kappa value) and sensitivity in assessing each osteoarthritis finding were calculated for each sequence., Results: The FRACTURE sequence had the highest diagnostic performance, with an accuracy of 0.85, sensitivity of 0.85, specificity of 0.84, and AUC of 0.84. These values were 0.84, 0.72, 0.91, and 0.80, respectively, for the PDw sequence, and 0.83, 0.72, 0.91, and 0.79, respectively, for the T2wFS sequence. The AUC did not significantly differ between the FRACTURE and PDw sequences (Delong test, p > 0.05), but did significantly differ between the FRACTURE and T2wFS sequences (p < 0.05). For all osteoarthritis findings, the FRACTURE sequence had the highest kappa values and the highest sensitivity., Conclusions: FRACTURE sequencing may be a promising tool for the diagnosis of TMJ osteoarthritis compared with other conventional sequences., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The present study investigated the acute mucosal reaction (AMR) after high-dose-rate interstitial brachytherapy at 54 Gy/9 fractions (HDR54) as monotherapy administered twice a day for tongue cancer in 13 patients, and attempted to validate HDR54 by comparing the AMR with that of our previously reported HDR at 60 Gy/10 fractions (HDR60), and low-dose-rate interstitial brachytherapy at approximately 70 Gy (LDR70)., Methods: The European Organization for Research on Treatment of Cancer/ Radiation Therapy Oncology Group scoring system with modifications (score: 1-4.5) was used to evaluate AMR. The time courses of the AMR scores of HDR54 were recorded. The time courses of the AMR of HDR54, HDR 60, and LDR70 were each divided into 6 phases and compared., Results: The number of cases in the HDR54 group with a lower score (1-2) at the time of the initial response was significantly higher (12 cases) than those in the HDR60 group (1 case) (p=0.0077) and LDR70 group (1 case) (p=0.0077). In the HDR54 group, the time between the end of treatment and appearance of the first response was significantly longer (median: 3 days) than those in the HDR60 group (median: 1 day) (p<0.001) and LDR70 group (median: 1 day) (p<0.001). No significant differences were observed in the maximum score, its duration, or other parameters., Conclusions: The results indicated that the AMR of HDR54 started later and was gentler and more easily tolerated than the other two methods, suggesting the validity of HDR54 in terms of AMR.

Objectives: The purpose of this study was to generate radiographs including dentigerous cysts by applying the latest generative adversarial network (GAN; StyleGAN3) to panoramic radiography., Methods: A total of 459 cystic lesions were selected, and 409 images were randomly assigned as training data and 50 images as test data. StyleGAN3 training was performed for 500 000 images. Fifty generated images were objectively evaluated by comparing them with 50 real images according to four metrics: Fréchet inception distance (FID), kernel inception distance (KID), precision and recall, and inception score (IS). A subjective evaluation of the generated images was performed by three specialists who compared them with the real images in a visual Turing test., Results: The results of the metrics were as follows: FID, 199.28; KID, 0.14; precision, 0.0047; recall, 0.00; and IS, 2.48. The overall results of the visual Turing test were 82.3%. No significant difference was found in the human scoring of root resorption., Conclusions: The images generated by StyleGAN3 were of such high quality that specialists could not distinguish them from the real images., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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., (© 2024. The Author(s), under exclusive licence to The Society of The Nippon Dental University.)

Objective: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images., Methods: A total of 68 TMJs with or without condylar OA in dentofacial deformity patients were tested to verify the consistencies and performances of DL models created using 252 TMJs with or without OA in TMJ disorder and dentofacial deformity patients; these models were used to diagnose OA on conventional panoramic (Con-Pa) images and open (Open-TMJ) and closed (Closed-TMJ) mouth TMJ projection images. The GoogLeNet and VGG-16 networks were used to create the DL models. For comparison, two dental residents with < 1 year of experience interpreting radiographs evaluated the same condyle data that had been used to test the DL models., Results: On Open-TMJ images, the DL models showed moderate to very good consistency, whereas the residents' demonstrated fair consistency on all images. The areas under the curve (AUCs) of both DL models on Con-Pa (0.84 for GoogLeNet and 0.75 for VGG-16) and Open-TMJ images (0.89 for both models) were significantly higher than the residents' AUCs (p < 0.01). The AUCs of the DL models on Open-TMJ images (0.89 for both models) were higher than the AUCs on Closed-TMJ images (0.72 for both models)., Conclusions: The DL models created in this study could help residents to interpret Con-Pa and Open-TMJ images in the diagnosis of condylar OA., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Objectives: The objective of this study was to enhance the visibility of soft tissues on cone-beam computed tomography (CBCT) using a CycleGAN network trained on CT images., Methods: Training and evaluation of the CycleGAN were conducted using CT and CBCT images collected from Aichi Gakuin University (α facility) and Osaka Dental University (β facility). Synthesized images (sCBCT) output by the CycleGAN network were evaluated by comparing them with the original images (oCBCT) and CT images, and assessments were made using histogram analysis and human scoring of soft-tissue anatomical structures and cystic lesions., Results: The histogram analysis showed that on sCBCT, soft-tissue anatomical structures showed significant shifts in voxel intensity toward values resembling those on CT, with the mean values for all structures approaching those of CT and the specialists' visibility scores being significantly increased. However, improvement in the visibility of cystic lesions was limited., Conclusions: Image synthesis using CycleGAN significantly improved the visibility of soft tissue on CBCT, with this improvement being particularly notable from the submandibular region to the floor of the mouth. Although the effect on the visibility of cystic lesions was limited, there is potential for further improvement through refinement of the training method., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Purpose: This study aimed to assess the performance of 2-dimensional (2D) imaging with microscopy coils in delineating teeth and periodontal tissues compared with conventional 3-dimensional (3D) imaging on a 3 T magnetic resonance imaging (MRI) unit., Materials and Methods: Twelve healthy participants (4 men and 8 women; mean age: 25.6 years; range: 20-52 years) with no dental symptoms were included. The left mandibular first molars and surrounding periodontal tissues were examined using the following 2 sequences: 2D proton density-weighted (PDw) images and 3D enhanced T1 high-resolution isotropic volume excitation (eTHRIVE) images. Two-dimensional MRI images were taken using a 3 T MRI unit and a 47 mm microscopy coil, while 3D MRI imaging used a 3 T MRI unit and head-neck coil. Oral radiologists assessed dental and periodontal structures using a 4-point Likert scale. Inter- and intra-observer agreement was determined using the weighted kappa coefficient. The Wilcoxon signed-rank test was used to compare 2D-PDw and 3D-eTHRIVE images., Results: Qualitative analysis showed significantly better visualization scores for 2D-PDw imaging than for 3D-eTHRIVE imaging (Wilcoxon signed-rank test). 2D-PDw images provided improved visibility of the tooth, root dental pulp, periodontal ligament, lamina dura, coronal dental pulp, gingiva, and nutrient tract. Inter-observer reliability ranged from moderate agreement to almost perfect agreement, and intra-observer agreement was in a similar range., Conclusion: Two-dimensional-PDw images acquired using a 3 T MRI unit and microscopy coil effectively visualized nearly all aspects of teeth and periodontal tissues., Competing Interests: Conflicts of Interest: None, (Copyright © 2024 by Korean Academy of Oral and Maxillofacial Radiology.)

The purpose of this study is to develop two-step deep learning models that can automatically detect implant regions on panoramic radiographs and identify several types of implants. A total of 1,574 panoramic radiographs containing 3675 implants were included. The implant manufacturers were Kyocera, Dentsply Sirona, Straumann, and Nobel Biocare. Model A was created to detect oral implants and identify the manufacturers using You Only Look Once (YOLO) v7. After preparing the image patches that cropped the implant regions detected by model A, model B was created to identify the implant types per manufacturer using EfficientNet. Model A achieved very high performance, with recall of 1.000, precision of 0.979, and F1 score of 0.989. It also had accuracy, recall, precision, and F1 score of 0.98 or higher for the classification of the manufacturers. Model B had high classification metrics above 0.92, exception for Nobel's class 2 (Parallel). In this study, two-step deep learning models were built to detect implant regions, identify four manufacturers, and identify implant types per manufacturer., (© 2024. The Author(s), under exclusive licence to The Society of The Nippon Dental University.)

Objectives: This study aimed to clarify the performance of magnetic resonance imaging (MRI)-based deep learning classification models in diagnosing temporomandibular joint osteoarthritis (TMJ-OA) and to compare the developed diagnostic assistance with human observers., Methods: The subjects were 118 patients who underwent MRI for examination of TMJ disorders. One hundred condyles with TMJ-OA and 100 condyles without TMJ-OA were enrolled. Deep learning was performed with four networks (ResNet18, EfficientNet b4, Inception v3, and GoogLeNet) using five-fold cross validation. Receiver operating characteristics (ROC) curves were drawn for each model and diagnostic metrics were determined. The performances of the four network models were compared using Kruskal-Wallis tests and post-hoc Scheffe tests, and ROCs between the best model and human were compared using chi-square tests, with p < 0.05 considered significant., Results: ResNet18 had areas under the curves (AUCs) of 0.91-0.93 and accuracy of 0.85-0.88, which were the highest among the four networks. There were significant differences in AUC and accuracy between ResNet and GoogLeNet (p = 0.0264 and p = 0.0418, respectively). The kappa values of the models were large, 0.95 for ResNet and 0.93 for EfficientNet. The experts achieved similar AUC and accuracy values ​​to the ResNet metrics, 0.94 and 0.85, and 0.84 and 0.84, respectively, but with a lower kappa of 0.67. Those of the dental residents showed lower values. There were significant differences in AUCs between ResNet and residents (p < 0.0001) and between experts and residents (p < 0.0001)., Conclusions: Using a deep learning model, high performance was confirmed for MRI diagnosis of TMJ-OA., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Traumatic spinal cord injury (TSCI) causes an insult to the central nervous system, often resulting in devastating temporary or permanent neurological impairment and disability, which places a substantial financial burden on the health-care system. This study aimed to clarify the up-to-date epidemiology and demographics of patients with TSCI treated at the largest SCI center in Japan. Data on all patients admitted to the Spinal Injuries Center with TSCI between May 2005 and December 2021 were prospectively collected using a customized, locally designed SCI database named the Japan Single Center Study for Spinal Cord Injury Database (JSSCI-DB). A total of 1152 patients were identified from the database. The study period was divided into the four- or five-year periods of 2005-2009, 2010-2013, 2014-2017, and 2018-2021 to facilitate the observation of general trends over time. Our results revealed a statistically significant increasing trend in age at injury. Since 2014, the average age of injury has increased to exceed 60 years. The most frequent spinal level affected by the injury was high cervical (C1-C4: 45.8%), followed by low cervical (C5-C8: 26.4%). Incomplete tetraplegia was the most common cause or etiology category of TSCI, accounting for 48.4% of cases. As the number of injuries among the elderly has increased, the injury mechanisms have shifted from high-fall trauma and traffic accidents to falls on level surfaces and downstairs. Incomplete tetraplegia in the elderly due to upper cervical TSCI has also increased over time. The percentage of injured patients with an etiology linked to alcohol use ranged from 13.2% (2005-2008) to 19% (2014-2017). Given that Japan has one of the highest aging populations in the world, epidemiological studies in this country will be very helpful in determining health insurance and medical costs and deciding strategies for the prevention and treatment of TSCI in future aging populations worldwide., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yokota et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study aimed to investigate how width affects the design evaluation of sliding boards with a new shape. Ten caregivers at a senior facility evaluated five types of boards every two weeks. The new shape board received high ratings as the width increased, with a SUS (System Usability Scale) score of 68.5 points (95% CI 60.6-76.4). Compared to the traditional rectangular board, which had a width of 250 mm and a thickness of 5 mm, the new shape board (with a central width of 163 mm and a thickness of 8 mm) received higher ratings by 0.68 points (95% CI 0.31-1.05)using a paired comparison method. The results of a quantitative study on the usability of sliding boards from the perspective of caregivers indicated that for similar board shapes with a length of 650 mm and a thickness of 8 mm, the evaluation increases as the width increases in the range of 130 mm to 163 mm at the center.

Spasticity-defined as involuntary movements caused by insult to upper motor neurons after spinal cord injury (SCI)-interferes with patients' activities of daily living. Spasticity is generally identified and managed in the chronic phase of SCI, but few reports have examined the onset of spasticity after injury. The purpose of this study is to elucidate serial changes in spasticity after SCI and clarify the timing of severe spasticity. We prospectively examined individuals with acute traumatic SCI admitted within two weeks after injury. Severity of spasticity was evaluated using the Modified Ashworth Scale (MAS) at 2, 4, 6, and 8 weeks, followed by 3, 4, 5, and 6 months after injury. After completing evaluation of the cohort, the patients were divided into two groups: a spasticity group with MAS scores ≥ 3 (marked increase in muscle tone through most of the range of motion (ROM)) in at least one joint movement within 6 months of injury and a control group with MAS scores ≤ 2 in all joint movements throughout the 6 months after injury. Neurological findings such as the American Spinal Injury Association (ASIA) Impairment Scale grades and ASIA motor scores were also assessed at all time points, and the correlations between the onset of spasticity, severity of spasticity, and neurological findings were analyzed. There were 175 patients with traumatic SCI who were assessed consecutively for 6 months after injury. The MAS scores of the group significantly increased over time until 4 months after injury. The spasticity group had significantly higher MAS scores compared with the control group as early as 2 weeks post-injury. We found that the patients with earlier onset of spasticity had higher final MAS scores. No correlation was found between the ASIA Impairment Scale grade and the onset of spasticity. Our results reveal that the development of severe spasticity may be predictable from as early as 2 weeks after SCI, suggesting that early therapeutic intervention to mitigate problematic spasticity may enhance the benefits of post-injury rehabilitation.

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., (Copyright © 2024 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Conflicts of Interest: None, (Copyright © 2024 by Korean Academy of Oral and Maxillofacial Radiology.)

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., Competing Interests: Conflicts of Interest: None, (Copyright © 2024 by Korean Academy of Oral and Maxillofacial Radiology.)

Objectives: We evaluated the time course of the American Spinal Cord Injury Association (ASIA) impairment scale (AIS) for up to three months in participants within 72 h after traumatic spinal cord injury (TSCI) with complete paralysis. We aimed to determine the most useful sacral-sparing examination (deep anal pressure [DAP], voluntary anal contraction [VAC], S4-5 light touch [LT], or pin prick [PP] sensation) in determining AIS grades., Design: Retrospective cohort study., Setting: Spinal Injuries Center, Fukuoka, Japan., Participants: Among 668 TSCI participants registered in the Japan Single Center study for Spinal Cord Injury Database (JSSCI-DB) between January 2012 and May 2020, we extracted the data of 80 patients with AIS grade A within 72 h after injury and neurological level of injury (NLI) at T12 or higher., Interventions: None., Outcome Measures: The sacral-sparing examination at the time of the change to incomplete paralysis was compared to the AIS determination using a standard algorithm and with each assessment including the VAC, DAP, S4-5LT, and S4-5PP examinations at the time of AIS functional change. Agreement among assessments was evaluated using weighted kappa coefficients. The relationship was evaluated using Spearman's rank correlation coefficients., Results: Fifteen participants (18.8%) improved to incomplete paralysis (AIS B to D) within three months after injury. The single assessment among the sacral-sparing examinations with the highest agreement and strongest correlation with AIS determination was the S4-5LT examination ( k  = 0.89, P  < 0.01, r  = 0.84, P  < 0.01)., Conclusions: The S4-5LT examination is key in determining complete or incomplete paralysis due to its high discriminatory power.

Ultrasonography is the preferred modality for detailed evaluation of enlarged lymph nodes (LNs) identified on computed tomography and/or magnetic resonance imaging, owing to its high spatial resolution. However, the diagnostic performance of ultrasonography depends on the examiner's expertise. To support the ultrasonographic diagnosis, we developed YOLOv7-based deep learning models for metastatic LN detection on ultrasonography and compared their detection performance with that of highly experienced radiologists and less experienced residents. We enrolled 462 B- and D-mode ultrasound images of 261 metastatic and 279 non-metastatic histopathologically confirmed LNs from 126 patients with head and neck squamous cell carcinoma. The YOLOv7-based B- and D-mode models were optimized using B- and D-mode training and validation images and their detection performance for metastatic LNs was evaluated using B- and D-mode testing images, respectively. The D-mode model's performance was comparable to that of radiologists and superior to that of residents' reading of D-mode images, whereas the B-mode model's performance was higher than that of residents but lower than that of radiologists on B-mode images. Thus, YOLOv7-based B- and D-mode models can assist less experienced residents in ultrasonographic diagnoses. The D-mode model could raise the diagnostic performance of residents to the same level as experienced radiologists.

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.

Objectives: To clarify the performance of transfer learning with a small number of Waters' images at institution B in diagnosing maxillary sinusitis, based on a source model trained with a large number of panoramic radiographs at institution A., Methods: The source model was created by a 200-epoch training process with 800 training and 60 validation datasets of panoramic radiographs at institution A using VGG-16. One hundred and eighty Waters' and 180 panoramic image patches with or without maxillary sinusitis at institution B were enrolled in this study, and were arbitrarily assigned to 120 training, 20 validation, and 40 test datasets, respectively. Transfer learning of 200 epochs was performed using the training and validation datasets of Waters' images based on the source model, and the target model was obtained. The test Waters' images were applied to the source and target models, and the performance of each model was evaluated. Transfer learning with panoramic radiographs and evaluation by two radiologists were undertaken and compared. The evaluation was based on the area of receiver-operating characteristic curves (AUC)., Results: When using Waters' images as the test dataset, the AUCs of the source model, target model, and radiologists were 0.780, 0.830, and 0.806, respectively. There were no significant differences between these models and the radiologists, whereas the target model performed better than the source model. For panoramic radiographs, AUCs were 0.863, 0.863, and 0.808, respectively, with no significant differences., Conclusions: This study performed transfer learning using a small number of Waters' images, based on a source model created solely from panoramic radiographs, resulting in a performance improvement to 0.830 in diagnosing maxillary sinusitis, which was equivalent to that of radiologists. Transfer learning is considered a useful method to improve diagnostic performance., (© 2022. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

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., (© 2022. The Author(s).)

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., Competing Interests: Conflicts of Interest: None, (Copyright © 2023 by Korean Academy of Oral and Maxillofacial Radiology.)

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., (© 2022. The Author(s), under exclusive licence to The Society of The Nippon Dental University.)

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., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2022. The Author(s).)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Orthodontic Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.

Purpose: The purpose of this study was to evaluate the effect of a lead block for alveolar bone protection in image-guided high-dose-rate interstitial brachytherapy for tongue cancer., Material and Methods: We treated 6 patients and delivered 5,400 cGy in 9 fractions using a lead block. Effects of lead block (median thickness, 4 mm) on dose attenuation by distance were visually examined using TG-43 formalism-based dose distribution curves to determine whether or not the area with the highest dose is located in the alveolar bone, where there is a high-risk of infection. Dose re-calculations were performed using TG-186 formalism with advanced collapsed cone engine (ACE) for inhomogeneity correction set to cortical bone density for the whole mandible and alveolar bone, water density for clinical target volume (CTV), air density for outside body and lead density, and silastic density for lead block and its' silicon replica, respectively., Results: The highest dose was detected outside the alveolar bone in five of the six cases. For dose-volume histogram analysis, median minimum doses delivered per fraction to the 0.1 cm 3 of alveolar bone (D0.1cm 3 TG-43, ACE-silicon, and ACE-lead ) were 344.3 (range, 262.9-427.4) cGy, 336.6 (253.3-425.0) cGy, and 169.7 (114.9-233.3) cGy, respectively. D0.1cm 3 ACE-lead was significantly lower than other parameters. No significant difference was observed between CTV-related parameters., Conclusions: The results suggested that using a lead block for alveolar bone protection with a thickness of about 4 mm, can shift the highest dose area to non-alveolar regions. In addition, it reduced D0.1cm 3 of alveolar bone to about half, without affecting tumor dose., Competing Interests: Ken Yoshida has received an honorarium from Chiyoda Technol Corporation. The other authors report no conflict of interest., (Copyright © 2022 Termedia.)

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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

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., (© 2021. The Author(s).)

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.

To investigate the use of transfer learning when applying a deep learning source model from one institution (institution A) to another institution (institution B) for creating effective models (target models) for the detection of maxillary sinuses and diagnosis of maxillary sinusitis on panoramic radiographs. In addition, to determine appropriate numbers of training data for the transfer learning. Source model was created using 350 panoramic radiographs from institution A as training data. Transfer learning was performed by adding 25, 50, 100, 150, or 225 panoramic radiographs as training data from institution B to the source model; this yielded the target models T25, T50, T100, T150 and T225. Each model was then evaluated using test data that comprised 40 images from institution A, 30 images from institution B. The performance indices (recall, precision and F1 score) for detecting the maxillary sinuses by the source model exceeded 0.98 when using test data A from institution A, but they deteriorated when using test data B from institution B. In the evaluation of target models using test data B, model T25 showed improved detection performance (recall of 0.967). The diagnostic performance of model T50 for maxillary sinusitis exceeded 0.9 in sensitivity. Transfer learning, which involves applying a small amount of data to the source model, yielded high performances in detecting the maxillary sinuses and diagnosing the maxillary sinusitis on panoramic radiographs. This study serves as a reference when adapting source models to other institutions., (© 2021. The Society of The Nippon Dental University.)

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.

This study was performed to evaluate the diagnostic performance of deep learning systems using ultrasonography (USG) images of the submandibular glands (SMGs) in three different conditions: obstructive sialoadenitis, Sjögren's syndrome (SjS), and normal glands. Fifty USG images with a confirmed diagnosis of obstructive sialoadenitis, 50 USG images with a confirmed diagnosis of SjS, and 50 USG images with no SMG abnormalities were included in the study. The training group comprised 40 obstructive sialoadenitis images, 40 SjS images, and 40 control images, and the test group comprised 10 obstructive sialoadenitis images, 10 SjS images, and 10 control images for deep learning analysis. The performance of the deep learning system was calculated and compared between two experienced radiologists. The sensitivity of the deep learning system in the obstructive sialoadenitis group, SjS group, and control group was 55.0%, 83.0%, and 73.0%, respectively, and the total accuracy was 70.3%. The sensitivity of the two radiologists was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%. This study revealed that the deep learning system was more sensitive than experienced radiologists in diagnosing SjS in USG images of two case groups and a group of healthy subjects in inflammation of SMGs.

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., (© 2021. The Author(s).)

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

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%.

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., Competing Interests: Conflicts of Interest: None, (Copyright © 2021 by Korean Academy of Oral and Maxillofacial Radiology.)

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.

Objective: To apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance., Methods: One hundred and fifty-nine metastatic and 517 non-metastatic lymph nodes on 365 CT images in 56 patients with oral squamous cell carcinoma were examined. The images were arbitrarily assigned to training, validation, and testing datasets. Using the neural network, 'DetectNet' for object detection, the training procedure was conducted for 1000 epochs. Testing image datasets were applied to the learning model, and the detection performance was calculated., Results: The learning curve indicated that the recall (sensitivity) for detecting metastatic and non-metastatic lymph nodes reached 90% and 80%, respectively, while the model performance recall by applying the test dataset was 73.0% and 52.5%, respectively. The recall for detecting level IB and Level II metastatic lymph nodes was relatively high., Conclusions: A system that has the potential to automatically detect cervical lymph nodes was constructed.

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%.