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.

Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs.

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.

Can temporomandibular joint osteoarthritis be diagnosed on MRI proton density weighted images with diagnostic support from the latest deep learning classification models?

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.

An attempt to generate panoramic radiographs including jaw cysts using StyleGAN3.

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.

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.

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 ß-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.

A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs.

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.

Efficacy of therapeutic exercise for temporomandibular disorders as assessed by magnetic resonance imaging: a case report.

[Purpose] The efficacy of exercise therapy in temporomandibular disorders has been recognized. Here, we present our experience with exercise therapy. [Participant and Methods] A 25-year-old female with a sudden onset of mouth opening limitation in October 2018 was admitted to our hospital in November 2018. Based on our initial findings, the patient was diagnosed with left disc derangement of the temporomandibular joint without reduction. A definitive diagnosis was established following magnetic resonance imaging in December 2018. Subsequently, range-of-motion exercises for the temporomandibular joint as passive movements and self-traction therapy as active movements were conducted. Magnetic resonance imaging was repeated 4 months after the first treatment. [Results] The temporomandibular joint disc remained in anterior dislocation during mouth opening and closing. The mouth opening joint motion was significantly improved compared to the pre-therapy range. The pain-related visual analog scale score also significantly improved. [Conclusion] The range of motion of the temporomandibular joint was improved by range-of-motion exercises for the temporomandibular joint, and was maintained and managed using self-traction therapy. Improvement of the range of motion was confirmed by magnetic resonance imaging.

Can sonographic features be efficacy predictors of robotic massage treatment for masseter and temporal muscle in patients with temporomandibular disorder with myofascial pain?

OBJECTIVE: The aim of this study was to detect sonographic predictors for the efficacy of massage treatment of masseter and temporal muscle in temporomandibular disorders (TMDs) patients with myofascial pain. METHODS: Thirty-seven TMD patients with myofascial pain (6 men and 31 women, a median age of 45 years) were enrolled. An oral rehabilitation robot massaged the patient's masseter and temporal muscles with a standard massage pressure of 10 N for 16 min. The standard treatment protocol was set five sessions every 2 weeks. The median total duration of treatment was 9.5 weeks. Efficacy of treatment was evaluated based on maximum mouth opening and visual analog scale scores of muscle pain and daily life impediments. The intramuscular echogenic bands and elasticity index ratios of the masseter muscles were evaluated on sonographic or sonoelastographic images obtained before treatment and after the third and last treatment sessions. RESULTS: The sonographic features detected different changes after the third treatment session between the therapy-effective and therapy-ineffective groups: in the therapy-effective group, the frequency of visibility of the distinct echogenic bands increased, and the elasticity index ratio decreased. CONCLUSION: Sonographic features after the third treatment session may be useful as predictors of therapeutic efficacy.

Potential clinical application of masseter and temporal muscle massage treatment using an oral rehabilitation robot in temporomandibular disorder patients with myofascial pain.

OBJECTIVES: To investigate the safety, suitable treatment regimen, and efficacy of masseter and temporal muscle massage treatment using an oral rehabilitation robot. METHODS: Forty-one temporomandibular disorder (TMD) patients with myofascial pain (8 men, 33 women, median age: 46 years) were enrolled. The safety, suitable massage regimen, and efficacy of this treatment were investigated. Changes in masseter muscle thickness were evaluated on sonograms. RESULTS: No adverse events occurred with any of the treatment sessions. Suitable massage was at pressure of 10 N for 16 minutes. Five sessions were performed every 2 weeks. Total duration of treatment was 9·5 weeks in median. Massage treatment was effective in 70·3% of patients. Masseter muscle thickness decreased with treatment in the therapy-effective group. CONCLUSION: This study confirmed the safety of massage treatment, and established a suitable regimen. Massage was effective in 70·3% of patients and appeared to have a potential as one of the effective treatments for myofascial pain.

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.

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

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

Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

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.

Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.

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.

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.

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.

Efficacy of massage treatment technique in masseter muscle hardness: robotic experimental approach.

The study aimed to clarify the masseter muscle hardness in patients with myofascial pain, to examine their changes after massage, and to analyze whether the hardness can be an index for massage treatment. Sixteen patients with myofascial pain (12 with unilateral and 4 with bilateral masseter muscle pain) and 24 healthy volunteers were enrolled in this study. The masseter hardness between patients and the healthy volunteers was compared. The changes in the hardness in patients after massage were examined. The relation of the hardness with massage regimens and efficacies was analyzed. There was a significant right-and-left difference of the hardness in patients, although there was no difference in the healthy volunteers. The hardness decreased after massage. The pretreatment asymmetry index of the hardness showed a significant correlation with the massage pressure. It was concluded that there was a significant difference between the right and left masseter hardness in patients with myofascial pain. After massage treatment, the masseter hardness and right-and-left difference decreased. The hardness may be an index for determining the massage pressure.

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.

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

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