Metagenomic analysis of oral and intestinal microbiome of patients during the initial stage of orthodontic treatment.

INTRODUCTION: This prospective study analyzed changes in the oral and intestinal microbiomes in patients before and after fixed orthodontic treatment, elucidating the impacts of fixed orthodontic treatment on patient health and metabolism. METHODS: Metagenomic analysis was conducted on stool, dental plaque, and saliva samples from 10 fixed orthodontic patients. All the samples were sequenced with Illumina NovaSeq 6000 with a paired-end sequencing length of 150 bp. Identification of taxa in metagenomes and functional annotation of genes of the microbiota were performed using the data after quality control. Clinical periodontal parameters, including the gingiva index, plaque index, and pocket probing depth, were examined at each time point in triplicates. Patients also received a table to record their oral hygiene habits of brushing, flossing, and dessert consumption frequency over 1 month. RESULTS: The brushing and flossing times per day of patients were significantly increased after treatment compared with baseline. The number of times a patient ate dessert daily was also fewer after treatment than at baseline. In addition, the plaque index decreased significantly, whereas the pH value of saliva, gingiva index, and pocket probing depth did not change. No significant differences were observed between the participants before and after orthodontic treatment regarding alpha-diversity analysis of the gut, dental plaque, or saliva microbiota. However, on closer analysis, periodontal disease-associated bacteria levels in the oral cavity remain elevated. Alterations in gut microbiota were also observed after orthodontic treatment. CONCLUSIONS: The richness and diversity of the microbiome did not change significantly during the initial stage of fixed orthodontic treatment. However, the levels of periodontal disease-associated bacteria increased.

Application of three-dimensional reconstruction technology in dentistry: a narrative review.

BACKGROUND: Three-dimensional(3D) reconstruction technology is a method of transforming real goals into mathematical models consistent with computer logic expressions and has been widely used in dentistry, but the lack of review and summary leads to confusion and misinterpretation of information. The purpose of this review is to provide the first comprehensive link and scientific analysis of 3D reconstruction technology and dentistry to bridge the information bias between these two disciplines. METHODS: The IEEE Xplore and PubMed databases were used for rigorous searches based on specific inclusion and exclusion criteria, supplemented by Google Academic as a complementary tool to retrieve all literature up to February 2023. We conducted a narrative review focusing on the empirical findings of the application of 3D reconstruction technology to dentistry. RESULTS: We classify the technologies applied to dentistry according to their principles and summarize the different characteristics of each category, as well as the different application scenarios determined by these characteristics of each technique. In addition, we indicate their development prospects and worthy research directions in the field of dentistry, from individual techniques to the overall discipline of 3D reconstruction technology, respectively. CONCLUSIONS: Researchers and clinicians should make different decisions on the choice of 3D reconstruction technology based on different objectives. The main trend in the future development of 3D reconstruction technology is the joint application of technology.

Influence of orthodontic treatment with premolar extraction on the spatial position of maxillary third molars in adult patients: a retrospective cohort cone-bean computed tomography study.

BACKGROUND: Based on low-dose radiation Cone-bean computed tomography (CBCT) images, This study aims to establish a space coordinate system, which offers more precise and comparable evaluation on changes of maxillary third molars influenced by orthodontic treatment with premolar extraction in adults. The system suggests promising application prospect in future studies related to CBCT superimposition and evaluation for its feasibility and efficiency. METHODS: Forty-nine maxillary third molars from 27 patients (mean age, 20.78 years) were included. CBCT images were obtained before and after orthodontic treatment with premolars extracted (mean treatment duration, 31.47 months). The changes in the position, angulation, and rotation of the third molars were evaluated with a space coordinate system using four landmarks: anterior nasal spine (ANS), posterior nasal spine (PNS), left and right orbitales. RESULTS: After orthodontic treatment, the third molars moved forward (adjusted mean, 1.44 mm) (p < 0.001) and downward (adjusted mean, 2.87 mm) (p < 0.001) accompanied by outward rotation of the crowns (adjusted mean, 5.38°) (p = 0.001), while changes in angulation were insignificant. CONCLUSIONS: This was the first study to systematically investigate the spatial position change of maxillary third molars in adult patients who received orthodontic treatment with premolar extraction. During the process, maxillary third molars moved downward and forward accompanied by outward rotation of the crowns. Orthodontists should take tooth movement potential into consideration when making extraction plans.

Artificial intelligence-aided detection of ectopic eruption of maxillary first molars based on panoramic radiographs.

OBJECTIVES: Ectopic eruption (EE) of maxillary permanent first molars (PFMs) is among the most frequent ectopic eruption, which leads to premature loss of adjacent primary second molars, impaction of premolars and a decrease in dental arch length. Apart from oral manifestations such asdelayed eruption of PFMs and discoloration of primary second molars, panoramic radiographs can reveal EE of maxillary PFMs as well. Identifying eruption anomalies in radiographs can be strongly experience-dependent, leading us to develop here an automatic model that can aid dentists in this task and allow timelier interventions. METHODS: Panoramic X-ray images from 1480 patients aged 4-9 years old were used to train an auto-screening model. Another 100 panoramic images were used to validate and test the model. RESULTS: The positive and negative predictive values of this auto-screening system were 0.86 and 0.88, respectively, with a specificity of 0.90 and a sensitivity of 0.86. Using the model to aid dentists in detecting EE on the 100 panoramic images led to higher sensitivity and specificity than when three experienced pediatric dentists detected EE manually. CONCLUSIONS: Deep learning-based automatic screening system is useful and promising in the detection EE of maxillary PFMs with relatively high specificity. However, deep learning is not completely accurate in the detection of EE. To minimize the effect of possible false negative diagnosis, regular follow-ups and re-evaluation are required if necessary. CLINICAL SIGNIFICANCE: Identification of EE through a semi-automatic screening model can improve the efficacy and accuracy of clinical diagnosis compared to human experts alone. This method may allow earlier detection and timelier intervention and management.

Accuracy of RGB-D camera-based and stereophotogrammetric facial scanners: a comparative study.

OBJECTIVES: This study aimed to evaluate and compare the accuracy and inter-operator reliability of a low-cost red-green-blue-depth (RGB-D) camera-based facial scanner (Bellus3D Arc7) with a stereophotogrammetry facial scanner (3dMD) and to explore the possibility of the former as a clinical substitute for the latter. METHODS: A mannequin head was selected as the research object. In the RGB-D camera-based facial scanner group, the head was continuously scanned five times using an RGB-D camera-based facial scanner (Bellus3D Arc7), and the outcome data of each scan was then imported into CAD software (MeshLab) to reconstruct three-dimensional (3D) facial photographs. In the stereophotogrammetry facial scanner group, the mannequin head was scanned with a stereophotogrammetry facial scanner (3dMD). Selected parameters were directly measured on the reconstructed 3D virtual faces using a CAD software. The same parameters were then measured directly on the mannequin head using the direct anthropometry (DA) method as the gold standard for later comparison. The accuracy of the facial scanners was evaluated in terms of trueness and precision. Trueness was evaluated by comparing the measurement results of the two groups with each other and with that of DA using equivalence tests and average absolute deviations, while precision and inter-operator reliability were assessed using the intraclass correlation coefficient (ICC). A 3D facial mesh deviation between the two groups was also calculated for further reference using a 3D metrology software (GOM inspect pro). RESULTS: In terms of trueness, the average absolute deviations between RGB-D camera-based and stereophotogrammetry facial scanners, between RGB-D camera-based facial scanner and DA, and between stereophotogrammetry facial scanner and DA were statistically equivalent at 0.50±0.27 mm, 0.61±0.42 mm, and 0.28±0.14 mm, respectively. Equivalence test results confirmed that their equivalence was within clinical requirements (<1 mm). The ICC for each parameter was approximately 0.999 in terms of precision and inter-operator reliability. A 3D facial mesh analysis suggested that the deviation between the two groups was 0.37±0.01 mm. CONCLUSIONS: For facial scanners, an accuracy of <1 mm is commonly considered clinically acceptable. Both the RGB-D camera-based and stereophotogrammetry facial scanners in this study showed acceptable trueness, high precision, and inter-operator reliability. A low-cost RGB-D camera-based facial scanner could be an eligible clinical substitute for traditional stereophotogrammetry. CLINICAL SIGNIFICANCE: The low-cost RGB-D camera-based facial scanner showed clinically acceptable trueness, high precision, and inter-operator reliability; thus, it could be an eligible clinical substitute for traditional stereophotogrammetry.

Artificial intelligence for caries and periapical periodontitis detection.

OBJECTIVES: Periapical periodontitis and caries are common chronic oral diseases affecting most teenagers and adults worldwide. The purpose of this study was to develop an evaluation tool to automatically detect dental caries and periapical periodontitis on periapical radiographs using deep learning. METHODS: A modified deep learning model was developed using a large dataset (4129 images) with high-quality annotations to support the automatic detection of both dental caries and periapical periodontitis. The performance of the model was compared to the classification performance of dentists. RESULTS: The deep learning model automatically distinguished dental caries with an F1-score of 0.829 and periapical periodontitis with an F1-score of 0.828. The comparison of model-only and expert-only detection performance showed that the accuracy of the fully automatic method was significantly higher than that of the young dentists. With deep learning assistance, the experts not only reached a higher diagnostic accuracy with an average F1-score of 0.7844 for dental caries and 0.8208 for periapical periodontitis compared to expert-only scenarios, but also increased inter-observer agreement from 0.585/0.590 to 0.726/0.713 for dental caries and from 0.623/0.563 to 0.752/0.740 for periapical periodontitis. CONCLUSIONS: Based on these experimental results, deep learning can improve the accuracy and consistency of evaluating dental caries and periapical periodontitis on periapical radiographs. CLINICAL SIGNIFICANCE: Deep learning models can improve accuracy and consistency and reduce the workload of dentists, making artificial intelligence a powerful tool for clinical practice.

Machine learning in orthodontics: Challenges and perspectives.

Artificial intelligence (AI) applications have significantly improved our everyday quality of life. The last decade has witnessed the emergence of up-and-coming applications in the field of dentistry. It is hopeful that AI, especially machine learning (ML), due to its powerful capacity for image processing and decision support systems, will find extensive application in orthodontics in the future. We performed a comprehensive literature review of the latest studies on the application of ML in orthodontic procedures, including diagnosis, decision-making and treatment. Machine learning models have been found to perform similar to or with even higher accuracy than humans in landmark identification, skeletal classification, bone age prediction, and tooth segmentation. Meanwhile, compared to human experts, ML algorithms allow for high agreement and stability in orthodontic decision-making procedures and treatment effect evaluation. However, current research on ML raises important questions regarding its interpretability and dataset sample reliability. Therefore, more collaboration between orthodontic professionals and technicians is urged to achieve a positive symbiosis between AI and the clinic.

The functions of mechanosensitive ion channels in tooth and bone tissues.

Tooth and bone are independent tissues with a close relationship. Both are composed of a highly calcified outer structure and soft inner tissue, and both are constantly under mechanical stress. In particular, the alveolar bone and tooth constitute an occlusion system and suffer from masticatory and occlusal force. Thus, mechanotransduction is a key process in many developmental, physiological and pathological processes in tooth and bone. Mechanosensitive ion channels such as Piezo1 and Piezo2 are important participants in mechanotransduction, but their functions in tooth and bone are poorly understood. This review summarizes our current understanding of mechanosensitive ion channels and their roles in tooth and bone tissues. Research in these areas may shed new light on the regulation of tooth and bone tissues and potential treatments for diseases affecting these tissues.

An injectable hydrogel with pH-sensitive and self-healing properties based on 4armPEGDA and N-carboxyethyl chitosan for local treatment of hepatocellular carcinoma.

Injectable hydrogels with pH-sensitive and self-healing properties have great application potential in the field of anti-cancer drug carriers. In this work, an injectable hydrogel is prepared using 4armPEG-benzaldehyde (4armPEGDA) and N-carboxyethyl chitosan (CEC) as a new drug carrier. The gelation time, equilibrium swelling rate, degradation time, and dynamic modulus of the injectable hydrogels can be adjusted by merely changing the concentration of 4armPEGDA. The volume of the hydrogel shrinks at pH 5.6 and expands at pH 7.4, which helps to control the release of anti-cancer drug. At pH 5.6, the hydrogels show a fast and substantial Dox release effect, which is five times higher than that at pH 7.4. In vitro cumulative drug release of all the hydrogels reached equilibrium on about the fourth day, and the hydrogel is completely degraded within five days, which contributes to the Dox-loaded hydrogel to further release the remaining Dox. Moreover, the Dox-loaded hydrogel shows a strong inhibitory effect on the growth of human hepatocellular carcinoma cells (HepG2). Finally, the anti-tumor model experiment in vivo demonstrated that the Dox-loaded hydrogel can significantly inhibit tumor growth within five days. Therefore, such injectable hydrogels are excellent carriers for the potential treatment of hepatocellular carcinoma.