The analysis of strut features in the endosteal margin area showed potential for the development of an osteoporosis detection model based on panoramic radiography.

Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and joint noise. Panoramic imaging can be used as a basic screening tool with thorough clinical examination in diagnosing TMJ OA. This paper proposes an algorithm that can extract the condylar region and determine its abnormality by using convolutional neural networks (CNNs) and Faster region-based CNNs (R-CNNs). Panoramic images are collected retrospectively and 1000 images are classified into three categories—normal, abnormal, and unreadable—by a dentist or orofacial pain specialist. Labels indicating whether the condyle is detected and its location enabled more clearly recognizable panoramic images. The uneven proportion of normal to abnormal data is adjusted by duplicating and rotating the images. An R-CNN model and a Visual Geometry Group-16 (VGG16) model are used for learning and condyle discrimination, respectively. To prevent overfitting, the images are rotated ±10° and shifted by 10%. The average precision of condyle detection using an R-CNN at intersection over union (IoU) >0.5 is 99.4% (right side) and 100% (left side). The sensitivity, specificity, and accuracy of the TMJ OA classification algorithm using a CNN are 0.54, 0.94, and 0.84, respectively. The findings demonstrate that classifying panoramic images through CNNs is possible. It is expected that artificial intelligence will be more actively applied to analyze panoramic X-ray images in the future.

Mammalian teeth have diverse pattern of the crown and root. The patterning mechanism of the root position and number is relatively unknown compared to that of the crown. The root number does not always match to the cusp number, which has prevented the complete understanding of root patterning. In the present study, to elucidate the mechanism of root pattern formation, we examined (1) the pattern of cervical tongues, which are tongue-like epithelial processes extending from cervical loops, (2) factors influencing the cervical tongue pattern and (3) the relationship among patterns of cusp, cervical tongue and root in multi-rooted teeth. We found a simple mechanism of cervical tongue formation in which the lateral growth of dental mesenchyme in the cuspal region pushes the cervical loop outward, and the cervical tongue appears in the intercuspal region subsequently. In contrast, when lateral growth was physically inhibited, cervical tongue formation was suppressed. Furthermore, by building simple formulas to predict the maximum number of cervical tongues and roots based on the cusp pattern, we demonstrated a positive relationship among cusp, cervical tongue and root numbers. These results suggest that the cusp pattern and the lateral growth of cusps are important in the regulation of the root pattern.

PurposeThe aim of this study was to compare the coordinates of anatomical landmarks on cone-beam computed tomographic (CBCT) images in varied head positions before and after reorientation using image analysis software.Materials and MethodsCBCT images were taken in a normal position and four varied head positions using a dry skull marked with 3 points where gutta percha was fixed. In each of the five radiographic images, reference points were set, 20 anatomical landmarks were identified, and each set of coordinates was calculated. Coordinates in the images from the normally positioned head were compared with those in the images obtained from varied head positions using statistical methods. Post-reorientation coordinates calculated using a three-dimensional image analysis program were also compared to the reference coordinates.ResultsIn the original images, statistically significant differences were found between coordinates in the normal-position and varied-position images. However, post-reorientation, no statistically significant differences were found between coordinates in the normal-position and varied-position images.ConclusionThe changes in head position impacted the coordinates of the anatomical landmarks in three-dimensional images. However, reorientation using image analysis software allowed accurate superimposition onto the reference positions.

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