Reconsideration of the horizontal tube-shifting technique in the intraoral radiography of maxillary molars.

Objective: The horizontal tube-shifting technique can be adopted to separate overlapping buccal roots of the maxillary molar from the palatal root. A simulation study was performed to determine an appropriate tube-shift angulation when adopting three-dimensional computed tomography imaging.

Methods: Cone-beam computed tomography images of 21 volunteers were used for simulation.

Sex differences and age-related changes in the mandibular alveolar bone mineral density using a computer-aided measurement system for intraoral radiography.

This study aimed to conduct a cross-sectional data analysis of the alveolar bone mineral density (al-BMD) in 225 patients of various ages and different sexes. The al-BMD value in the mandibular incisor region was calculated using a computer-aided measurement system (DentalSCOPE) for intraoral radiography. All participants with intact teeth (101 males and 124 females; age range, 25-89 years) were divided into three age-segregated groups (25-49, 50-74, and > 75 years).

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".

Deep learning and artificial intelligence in dental diagnostic imaging.

The application of artificial intelligence (AI) based on deep learning in dental diagnostic imaging is increasing. Several popular deep learning tasks have been applied to dental diagnostic images. Classification tasks are used to classify images with and without positive abnormal findings or to evaluate the progress of a lesion based on imaging findings.

Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning.

Objectives: A videofluoroscopic swallowing study (VFSS) is conducted to detect aspiration. However, aspiration occurs within a short time and is difficult to detect. If deep learning can detect aspirations with high accuracy, clinicians can focus on the diagnosis of the detected aspirations.

Variance of radiographical alveolar bone mineral density by the anatomical morphology of mandibular bone.

Purpose: Evaluating the bone mineral density (BMD) of alveolar bone is useful for dental treatments. The DentalSCOPE is an image analysis system developed to evaluate the BMD of alveolar bone. The aim of this study was to evaluate the relationship between cross-sectional anatomical size and BMD value.

Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques.

Objective: The aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs.

Study Design: The panoramic radiographs containing the mandibular canal and impacted third molar were collected from 2 hospitals (Hospitals A and B). A total of 3200 areas were used for creating and evaluating learning models.

Deep learning for preliminary profiling of panoramic images.

Objective: This study explored the feasibility of using deep learning for profiling of panoramic radiographs.

Study Design: Panoramic radiographs of 1000 patients were used. Patients were categorized using seven dental or physical characteristics: age, gender, mixed or permanent dentition, number of presenting teeth, impacted wisdom tooth status, implant status, and prosthetic treatment status.

Tooth recognition of 32 tooth types by branched single shot multibox detector and integration processing in panoramic radiographs.

The purpose of our study was to analyze dental panoramic radiographs and contribute to dentists' diagnosis by automatically extracting the information necessary for reading them. As the initial step, we detected teeth and classified their tooth types in this study. We propose single-shot multibox detector (SSD) networks with a side branch for 1-class detection without distinguishing the tooth type and for 16-class detection (i.

Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.

Objectives: The aims of the present study were to construct a deep learning model for automatic segmentation of the temporomandibular joint (TMJ) disc on magnetic resonance (MR) images, and to evaluate the performances using the internal and external test data.

Methods: In total, 1200 MR images of closed and open mouth positions in patients with temporomandibular disorder (TMD) were collected from two hospitals (Hospitals A and B). The training and validation data comprised 1000 images from Hospital A, which were used to create a segmentation model.

A novel algorithm for extracting soft-tissue and bone images measured using a photon-counting type X-ray imaging detector with the help of effective atomic number analysis.

Most of the objects targeted for X-ray examination are composed of soft-tissue and bone. We aimed to develop an algorithm for generating X-ray images which can give quantitative information of soft-tissue and bone using an energy-resolving photon-counting type imaging detector. We used polychromatic X-rays for analysis in which both the beam hardening effect and detector response were properly corrected and then succeeded in virtually treating the amount of measured X-ray attenuation as if it were measured using monochromatic X-rays.

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

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

Methods: We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively.

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.

Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle.

Objective: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities.

Methods: Panoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

Clinical guidelines for the application of panoramic radiographs in screening for osteoporosis.

Osteoporotic fractures are associated with an increased risk of subsequent fractures, a higher rate of mortality, and incremental medical costs. Incidental findings, which include some measurements related to the mandibular inferior cortex and the alveolar trabecular bone pattern of the mandible determined on panoramic radiographs, are considered to be a useful tool for identifying asymptomatic individuals at risk of having osteoporosis and/or fragility fractures. We undertook a worldwide literature survey and present the following clinical recommendations.

Effective atomic number image determination with an energy-resolving photon-counting detector using polychromatic X-ray attenuation by correcting for the beam hardening effect and detector response.

In this study, we propose an effective atomic number (Z) determination method based on a photon-counting technique. The proposed method can correct for the beam hardening effect and detector response based on polychromatic X-rays to allow high accuracy material identification. To demonstrate the effectiveness of our method, the procedure was applied to X-ray images acquired by a prototype energy-resolving photon-counting detector and we obtained an Z image with accuracy of Z ± 0.

Mandibular Radiomorphometric Indices and Tooth Loss as Predictors for the Risk of Osteoporosis using Panoramic Radiographs.

Purpose: To assess the mandibular cortical width (MCW) and morphology of the mandibular inferior cortex (MIC) on panoramic views from a large sample of males and females in various age groups by using an automated morphometric grading system for assisting osteoporosis screening. Furthermore, possible predictors and concrete cut-off values to identify the risk for osteoporosis were evaluated.

Materials And Methods: MCW, MIC, tooth loss (TL), and alveolar bone loss (ABL) were retrospectively evaluated in 700 panoramic images from dental patients in Hong Kong using commercially available software.

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.

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.

Computer programme to assess mandibular cortex morphology in cases of medication-related osteonecrosis of the jaw with osteoporosis or bone metastases.

Purpose: The purpose of this study was to evaluate the morphology of the mandibular cortex in cases of medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis or bone metastases using a computer programme.

Materials And Methods: Fifty-four patients with MRONJ (35 with osteoporosis and 19 with bone metastases) were examined using panoramic radiography. The morphology of the mandibular cortex was evaluated using a computer programme that scanned the mandibular inferior cortex and automatically assessed the mandibular cortical index (MCI) according to the thickness and roughness of the mandibular cortex, as follows: normal (class 1), mildly to moderately eroded (class 2), or severely eroded (class 3).

Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data.

Objectives: Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts.