The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis.

Context Aware Convolutional Neural Network for Children Caries Diagnosis on Dental Panoramic Radiographs: The objective of this study is to improve traditional convolutional neural networks for more accurate children dental caries diagnosis on panoramic radiographs. A context aware convolutional neural network (CNN) is proposed by considering information among adjacent teeth, based on the fact that caries of teeth often affects each other due to the same growing environment. Specifically, when performing caries diagnosis on a tooth, information from its adjacent teeth will be collected and adaptively fused for final classification. Children panoramic radiographs of 210 patients with one or more caries and 94 patients without caries are utilized, among which there are a total of 6028 teeth with 3039 to be caries. The proposed context aware CNN outperforms typical CNN baseline with the accuracy, precision, recall, score, and area-under-the-curve (AUC) being 0.8272, 0.8538, 0.8770, 0.8652, and 0.9005, respectively, showing potential to improve typical CNN instead of just copying them in previous works. Specially, the proposed method performs better than two five-year attending doctors for the second primary molar caries diagnosis. Considering the results obtained, it is beneficial to promote CNN based deep learning methods for assisting dentists for caries diagnosis in hospitals.

Background: Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status. Methods: We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 participants younger than 24 years old. The midpalatal suture region of interest (ROI) labeling was completed by two experienced clinical experts. The CBCT image fusion algorithm and image texture feature analysis algorithm were constructed and optimized. The age range prediction convolutional neural network (CNN) was conducted and tested. Results: The midpalatal suture fusion images contain complete semantic information for appraising midpalatal suture maturation and ossification status during the fast growth and development period. Correlation and homogeneity are the two texture features with the strongest relevance to chronological age. The overall performance of the age range prediction CNN model is satisfactory, especially in the 4 to 10 years range and the 17 to 23 years range, while for the 13 to 14 years range, the model performance is compromised. Conclusions: The image fusion algorithm can help show the overall perspective of the midpalatal suture in one fused image effectively. Furthermore, clinical decisions for maxillary transverse deficiency should be appraised by midpalatal suture image features directly rather than by age, especially in the 13 to 14 years range.

The midpalatal suture is mainly responsible for the growth and development of the maxillary and resistance to rapid maxillary expansion (RME). It is essential for clinical researchers to explore the intramembrane ossification and to elucidate the underlying mechanism of the maturation and ossification process of the midpalatal suture to help identify the optimum time and force of RME. However, mechanistic studies associated with the midpalatal suture are rare. The aim of this present study is to create an intramembrane osteogenesis model for the midpalatal suture region of mice. Interestingly, we discovered a type of chondrogenic mesenchymal cell expressing Piezo2, which might be related to the detection of mechanical and external stimuli. This result provides a potential molecular and cellular mechanism that explains why the midpalatal suture is not closed until adulthood. We depict a landscape of mesenchymal cells that might play an important role in the intramembrane osteogenesis of the midpalatal suture and provide new perspectives on midpalate suture maturation and ossification, which might lead to further possibilities for clinical operations.

Asthma and dental caries are the two most common diseases in children, and the relationship between them has been a focus of research. Whether dental caries affects the development of asthma has long been controversial. The aim of this study was to conduct a systematic review of the literature to assess the effect of dental caries on the development of asthma and provide new ideas for the pathogenesis and promoting factors of asthma. For a systematic review and meta-analysis, we systematically searched three databases (PubMed, Web of Science, and Embase) for studies published from database inception to 22 May 2022. We included observational studies that investigated the effect of dental caries on the development of asthma. Studies were critically appraised and a meta-analysis was performed to estimate a pooled effect. From the 845 studies initially identified, 7 of these were included in the meta-analysis. Included studies originated from America (n = 5) and Asia (n = 2). A meta-analysis of data from seven selected studies suggested that dental caries was positively associated with the risk of asthma development (The odds ratio for the pooled effect was 1.06, 95% confidence interval: 1.01, 1.10). In addition, the effect of dental caries on asthma risk varied in different geographic locations according to subgroup analyses. This study suggests that dental caries may affect the development of asthma and emphasizes the importance of increased awareness of dental care and caries prevention in patients with asthma.

Panoramic radiograph is one of the most widely used inspection tools for dentists making caries diagnosis, especially for teeth that are hard to be diagnosed through visual inspection. Recently, several deep learning methods, e.g., based on convolutional neural network (CNN) or transformer network, have been proposed for automatic caries diagnosis on dental panoramic radiographs, and promising results have been achieved. However, current approaches use all the teeth equally when training their models, which results in performance degeneration because of unbalanced classification difficulties for different tooth positions. The objective of this study is to introduce a position weighted CNN to alleviate the above problem for more accurate caries diagnosis. The position weighted module evaluates and revises the output of a specially designed CNN to incorporate position information. In addition, a novel data augmentation method is used to balance data with uneven decayed and normal teeth, which is one of the reasons leading to unbalanced classification difficulty. To verify the proposed method, a children panoramic radiograph database is collected and labeled with more than 6,000 teeth. The proposed approach outperforms the state-of-the-art caries diagnosis methods with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8859, 0.8875, 0.8932, 0.8903 and 0.9315, respectively. Specially, the proposed model displays higher diagnosis performance compared with two attending doctors with more than five-year clinical experience but with different diagnosis patterns, showing a potential tool for assisting dentists.