Image quality-based dose optimization in pediatric cone-beam computed tomography: A pilot methodological study.

Purpose: This study aimed to propose a methodological approach for reducing the radiation dose in pediatric conebeam computed tomography (CBCT), focusing exclusively on balancing image quality with dose optimization.

Materials And Methods: The dose-area product (DAP) for exposure was reduced using copper-plate attenuation of an X-ray source. The thickness of copper (Cu) was increased from 0 to 2.

How well do large language model-based chatbots perform in oral and maxillofacial radiology?

Objectives: This study evaluated the performance of four large language model (LLM)-based chatbots by comparing their test results with those of dental students on an oral and maxillofacial radiology examination.

Methods: ChatGPT, ChatGPT Plus, Bard, and Bing Chat were tested on 52 questions from regular dental college examinations. These questions were categorized into three educational content areas: basic knowledge, imaging and equipment, and image interpretation.

Perceptions and attitudes of dental students and dentists in South Korea toward artificial intelligence: a subgroup analysis based on professional seniority.

Background: This study explored dental students' and dentists' perceptions and attitudes toward artificial intelligence (AI) and analyzed differences according to professional seniority.

Methods: In September to November 2022, online surveys using Google Forms were conducted at 2 dental colleges and on 2 dental websites. The questionnaire consisted of general information (8 or 10 items) and participants' perceptions, confidence, predictions, and perceived future prospects regarding AI (17 items).

Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort.

Developing a deep-learning-based diagnostic model demands extensive labor for medical image labeling. Attempts to reduce the labor often lead to incomplete or inaccurate labeling, limiting the diagnostic performance of models. This paper (i) constructs an attention-guiding framework that enhances the diagnostic performance of jaw bone pathology by utilizing attention information with partially labeled data; (ii) introduces an additional loss to minimize the discrepancy between network attention and its label; (iii) introduces a trapezoid augmentation method to maximize the utility of minimally labeled data.

Evaluation of masticatory muscles in temporomandibular joint disorder patients using quantitative MRI fat fraction analysis-Could it be a biomarker?

Temporomandibular joint disorders (TMDs) are closely related to the masticatory muscles, but objective and quantitative methods to evaluate muscle are lacking. IDEAL-IQ, a type of chemical shift-encoded magnetic resonance imaging (CSE-MRI), can quantify the fat fraction (FF). The purpose of this study was to develop an MR IDEAL-IQ-based method for quantitative muscle diagnosis in TMD patients.

CT and MR imaging findings of head and neck chondrosarcoma.

Objectives: This study investigated the imaging features of head and neck chondrosarcoma (HNCS) according to its origin and pathologic subtype.

Methods: Patients who were pathologically diagnosed with HNCS between January 2000 and April 2022 were retrospectively reviewed. Lesions were classified based on their origin and pathologic subtype.

Automatic diagnosis of true proximity between the mandibular canal and the third molar on panoramic radiographs using deep learning.

Evaluating the mandibular canal proximity is crucial for planning mandibular third molar extractions. Panoramic radiography is commonly used for radiological examinations before third molar extraction but has limitations in assessing the true contact relationship between the third molars and the mandibular canal. Therefore, the true relationship between the mandibular canal and molars can be determined only through additional cone-beam computed tomography (CBCT) imaging.

Development and accuracy validation of a fat fraction imaging biomarker for sialadenitis in the parotid gland.

Background: The diagnosis of sialadenitis, the most frequent disease of the salivary glands, is challenging when the symptoms are mild. In such cases, biomarkers can be used as definitive diagnostic indicators. Recently, biomarkers have been developed by extracting and analyzing pathological and morphological features from medical imaging.

Radiomics-based sialadenitis staging in contrast-enhanced computed tomography and ultrasonography: a preliminary rat model study.

Objective: The aim of this study was to measure the ability of radiomics analysis to diagnose different stages of sialadenitis, compare the diagnostic accuracy of computed tomography (CT) and ultrasonography (US), and suggest radiomics features selected through 3 machine learning algorithms that would be helpful in discriminating between stages of sialadenitis with both imaging systems.

Study Design: Wistar rats were treated to induce acute and chronic sialadenitis in the left and right submandibular glands, respectively. Contrast-enhanced CT and US of the glands were performed, followed by extirpation and histopathologic confirmation.

Multi-planar 2.5D U-Net for image quality enhancement of dental cone-beam CT.

Cone-beam computed tomography (CBCT) can provide 3D images of a targeted area with the advantage of lower dosage than multidetector computed tomography (MDCT; also simply referred to as CT). However, in CBCT, due to the cone-shaped geometry of the X-ray source and the absence of post-patient collimation, the presence of more scattering rays deteriorates the image quality compared with MDCT. CBCT is commonly used in dental clinics, and image artifacts negatively affect the radiology workflow and diagnosis.

Refinement of image quality in panoramic radiography using a generative adversarial network.

Objective: We aimed to develop and assess the clinical usefulness of a generative adversarial network (GAN) model for improving image quality in panoramic radiography.

Methods: Panoramic radiographs obtained at Yonsei University Dental Hospital were randomly selected for study inclusion ( = 100). Datasets with degraded image quality ( = 400) were prepared using four different processing methods: blur, noise, blur with noise, and blur in the anterior teeth region.

Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging.

Cone-beam computed tomography (CBCT) produces high-resolution of hard tissue even in small voxel size, but the process is associated with radiation exposure and poor soft tissue imaging. Thus, we synthesized a CBCT image from the magnetic resonance imaging (MRI), using deep learning and to assess its clinical accuracy. We collected patients who underwent both CBCT and MRI simultaneously in our institution (Seoul).

Korean dental hygiene students' perceptions and attitudes toward artificial intelligence: An online survey.

Objectives: This study investigated Korean dental hygiene students' perceptions and attitudes toward artificial intelligence (AI) and aimed to identify needs for education to strengthen professional competencies.

Methods: A 24-question online survey was conducted to the dental hygiene students from four Korean schools in 2021. The questionnaire included seven questions on basic characteristics and 17 AI-related questions on the student's attitudes toward AI, the confidence in AI, predictions about AI, and its future prospects.

Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.

The evaluation of the maxillary sinus is very important in dental practice such as tooth extraction and implantation because of its proximity to the teeth, but it is not easy to evaluate because of the overlapping structures such as the maxilla and the zygoma on panoramic radiographs. When doom-shaped retention pseudocysts are observed in sinus on panoramic radiographs, they are often misdiagnosed as cysts or tumors, and additional computed tomography is performed, resulting in unnecessary radiation exposure and cost. The purpose of this study was to develop a deep learning model that automatically classifies retention pseudocysts in the maxillary sinuses on panoramic radiographs.

Parotid gland evaluation of menopausal women with xerostomia using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) method of MRI: a pilot study.

Objectives: This study aimed to analyze the quantitative fat fraction (FF) of the parotid gland in menopausal females with xerostomia using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL-IQ) method.

Methods: A total 138 parotid glands of 69 menopausal females were enrolled in our study and participants were divided into normal group and xerostomia group. The xerostomia group was divided into those with or without Sjögren's syndrome.

Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study.

Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers.

Impact of physiological parameters on the parotid gland fat fraction in a normal population.

Quantifying physiological fat tissue in the organs is important to further assess the organ's pathologic status. This study aimed to investigate the impact of body mass index (BMI), age, and sex on the fat fraction of normal parotid glands. Patients undergoing magnetic resonance imaging (MRI) of iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL-IQ) due to non-salivary gland-related disease were reviewed.

Synthesis of T2-weighted images from proton density images using a generative adversarial network in a temporomandibular joint magnetic resonance imaging protocol.

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint (TMJ) magnetic resonance imaging (MRI) protocol.

Materials And Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used.

Unintentional temporomandibular joint disc reduction after orthognathic surgery: A case report with long-term imaging follow-up.

This report presents a rare case where a displaced temporomandibular joint (TMJ) disc was reduced to its normal position after orthognathic surgery, and long-term magnetic resonance imaging (MRI) follow-up visualized these postoperative changes. A 22-year-old male patient presented for facial asymmetry. He also complained of pain in the right TMJ area, and MRI showed disc displacements in both TMJs.

Harmonization of robust radiomic features in the submandibular gland using multi-ultrasound systems: a preliminary study.

Objective: This study aimed to identify robust radiomic features in multiultrasonography of the submandibular gland and normalize the interdevice discrepancies by applying a machine-learning-based harmonization method.

Methods: Ultrasonographic images of normal submandibular gland of young healthy adults, aged between 20 and 40 years, were selected from two different devices. In a total of 30 images, the region of interest was determined along the border of gland parenchyma, and 103 radiomic features were extracted using A-VIEW.

Comparison of the clinical usefulness of structured and free-text reports for interpretation of jaw lesions on cone beam computed tomography images.

Objective: This study compared the clinical usefulness of structured reports (SRs) and free-text reports (FTRs) of lesions depicted on cone beam computed tomography (CBCT) images from the perspectives of report providers and receivers.

Study Design: In total, 36 CBCT images of jaw lesions obtained between February 2020 and August 2020 were evaluated. A working group of 3 oral and maxillofacial radiologists (OMRs) established a reporting system and prepared reports.

Performance comparison of three deep learning models for impacted mesiodens detection on periapical radiographs.

This study aimed to develop deep learning models that automatically detect impacted mesiodens on periapical radiographs of primary and mixed dentition using the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms and to compare their performance. Periapical radiographs of 600 pediatric patients (age range, 3-13 years) with mesiodens were used as a training and validation dataset. Deep learning models based on the YOLOv3, RetinaNet, and EfficientDet-D3 algorithms for detecting mesiodens were developed, and each model was trained 300 times using training (540 images) and validation datasets (60 images).