Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them.

Surgeons routinely interpret preoperative radiographic images for estimating the shape and position of the tooth prior to performing tooth extraction. In this study, we aimed to predict the difficulty of lower wisdom tooth extraction using only panoramic radiographs. Difficulty was evaluated using the modified Parant score.

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.

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.

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

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

Methods: Electronic databases (MEDLINE, EMBASE, the Cochrane Library, and Web of Science: Science Citation Index) were searched to identify studies using USE to diagnose SjS from database inception to 15 July 2022. The primary outcome was improved diagnostic accuracy for SjS with the use of USE.

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

A cemental tear (CeT) is a definitive clinical entity and its radiographic appearance is well known in single-rooted teeth. However, the imaging features of CeT in multi-rooted teeth have not been clarified. We report a case of CeT which arose in the maxillary first molar and exhibited an unusual appearance in cone-beam computed tomography images.

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.

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

Objectives: The aim of the present study was to create effective deep learning-based models for diagnosing the presence or absence of cleft palate (CP) in patients with unilateral or bilateral cleft alveolus (CA) on panoramic radiographs.

Methods: The panoramic images of 491 patients who had unilateral or bilateral cleft alveolus were used to create two models. Model A, which detects the upper incisor area on panoramic radiographs and classifies the areas into the presence or absence of CP, was created using both object detection and classification functions of DetectNet.

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

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

Methods: CT images of 158 metastatic and 514 non-metastatic lymph nodes were prepared. CT images were assigned to training, validation, and test datasets.

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

Objectives: The purpose of this study was to evaluate the difference in performance of deep-learning (DL) models with respect to the image classes and amount of training data to create an effective DL model for detecting both unilateral cleft alveoli (UCAs) and bilateral cleft alveoli (BCAs) on panoramic radiographs.

Methods: Model U was created using UCA and normal images, and Model B was created using BCA and normal images. Models C1 and C2 were created using the combined data of UCA, BCA, and normal images.

Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images.

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.

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

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

Study Design: We used data from 2 institutions (A and B) to create the models for use in institution B. In total, 224 panoramic radiographs with sialoliths were used.

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

Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet.

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

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

Materials And Methods: This study involved 3 inexperienced radiologists with different levels of experience, who received training 1 or 2 days a week under the supervision of experienced radiologists. The training program included collecting patient histories and performing physical and imaging examinations for various maxillofacial diseases.

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

Objectives: This study aimed to examine the performance of deep learning object detection technology for detecting and identifying maxillary cyst-like lesions on panoramic radiography.

Methods: Altogether, 412 patients with maxillary cyst-like lesions (including several benign tumors) were enrolled. All panoramic radiographs were arbitrarily assigned to the training, testing 1, and testing 2 datasets of the study.

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

Objective: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses.

Methods: The imaging data for healthy maxillary sinuses (587 sinuses, Class 0), inflamed maxillary sinuses (416 sinuses, Class 1), cysts of maxillary sinus regions (171 sinuses, Class 2) were assigned to training, testing 1, and testing 2 data sets.

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

Objective: This investigation aimed to verify and compare the performance of 3 deep learning systems for classifying maxillary impacted supernumerary teeth (ISTs) in patients with fully erupted incisors.

Study Design: In total, the study included 550 panoramic radiographs obtained from 275 patients with at least 1 IST and 275 patients without ISTs in the maxillary incisor region. Three learning models were created by using AlexNet, VGG-16, and DetectNet.

Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.

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.

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

Objective: The aim of this study was to compare time and storage space requirements, diagnostic performance, and consistency among 3 image recognition convolutional neural networks (CNNs) in the evaluation of the relationships between the mandibular third molar and the mandibular canal on panoramic radiographs.

Study Design: Of 600 panoramic radiographs, 300 each were assigned to noncontact and contact groups based on the relationship between the mandibular third molar and the mandibular canal. The CNNs were trained twice by using cropped image patches with sizes of 70 × 70 pixels and 140 × 140 pixels.

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

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

Methods: Panoramic and CT images from 11 patients with histologically verified SC of the temporomandibular joint were investigated. Based on the panoramic images, the patients were classified into a distinct group (5 patients) with typical features of calcified loose bodies and an indistinct group (6 patients) without such bodies.

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

Objectives: We evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists.

Methods: 100 patients with a confirmed diagnosis of SjS according to both the Japanese criteria and American-European Consensus Group criteria and 100 non-SjS patients that had a dry mouth and suspected SjS but were definitively diagnosed as non-SjS were enrolled in this study. All the patients underwent US scans of both the parotid glands (PG) and submandibular glands (SMG).

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

Objective: The aim of this study was to investigate whether a deep learning object detection technique can automatically detect and classify radiolucent lesions in the mandible on panoramic radiographs.

Study Design: Panoramic radiographs of patients with mandibular radiolucent lesions of 10 mm or greater, including ameloblastomas, odontogenic keratocysts, dentigerous cysts, radicular cysts, and simple bone cysts, were included. Lesion labels, including region of interest coordinates, were created in text format.

Neck recurrence and mortality in esthesioneuroblastoma: Implications for management of the N0 neck.

Objectives/hypothesis: To review the literature on neck recurrence in esthesioneuroblastoma.

Study Design: PubMed database.

Methods: A PubMed database search was performed using keywords "esthesioneuroblastoma," "olfactory neuroblastoma," and "esthesioneuroblastoma neck metastasis.