A mobile-phone-based diagnostic tool, which most of the population can easily access, could be a game changer in increasing the number of examinations of people with dental caries. This study aimed to apply a deep learning algorithm in diagnosing the stages of smooth surface caries via smartphone images. Materials and methods: A training dataset consisting of 1902 photos of the smooth surface of teeth taken with an iPhone 7 from 695 people was used. Four deep learning models, consisting of Faster Region-Based Convolutional Neural Networks (Faster R-CNNs), You Only Look Once version 3 (YOLOv3), RetinaNet, and Single-Shot Multi-Box Detector (SSD), were tested to detect initial caries lesions and cavities. The reference standard was the diagnosis of a dentist based on image examination according to the International Caries Classification and Management System (ICCMS) classification. Results: For cavitated caries, YOLOv3 and Faster R-CNN showed the highest sensitivity among the four tested models, at 87.4% and 71.4%, respectively. The sensitivity levels of these two models were only 36.9 % and 26% for visually non-cavitated (VNC). The specificity of the four models reached above 86% for cavitated caries and above 71% for VNC. Conclusion: The clinical application of YOLOv3 and Faster R-CNN models for diagnosing dental caries via smartphone images was promising. The current study provides a preliminary insight into the potential translation of AI from the laboratory to clinical practice.

Context and background: Complex fuzzy theory has a strong practical implication in many real-world applications. Complex Fuzzy Inference System (CFIS) is a powerful technique to overcome the challenges of uncertain, periodic data. However, a question is raised for CFIS: How can we deduce and predict the result in case there is little knowledge about data information and rule base? This is significance because many real applications do not have enough knowledge of rule base for inference so that the performance of systems may be low. Thus, it is necessary to have an approximate reasoning method to represent and derive final results. Motivation: Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been proposed with a specific inference mechanism according to the Mamdani type. A new improvement so-called the Mamdani Complex Fuzzy Inference System with Rule Reduction (M-CFIS-R) has been designed to utilize granular computing with complex similarity measures to reduce the rule base so as to gain better performance in decision-making problems. However in M-CFIS-R, testing data are checked by matching with each rule in the rule base, which leads to a high cost of computational time. Besides, if the testing data contain records that are not inferred by the rule base, the output cannot be generated. This happens in real commerce systems in which the rule base is small at the time of creation and needs to feed with new rules. Methodology: In order to handle those issues, this paper first time proposes the Fuzzy Knowledge Graph to represent the rule base in terms of linguistic labels and their relationships according to the rule set. An adjacent matrix of Fuzzy Knowledge Graph is generated for inference. When a record in the Testing dataset is given, it would be fuzzified and labelled. Each component in the record is checked with the Fuzzy Knowledge Graph by the inference mechanism in approximate reasoning called Fast Inference Search Algorithm. Then, we derive the label of the new record by the Max-Min operator. Besides, we also propose four extensions of Mamdani Complex Fuzzy Inference System Rule Reduction including Sugeno Complex Fuzzy Inference Systems, Tsukamoto Complex Fuzzy Inference Systems, Complex Fuzzy Measures and Complex Fuzzy Integrals in M-CFIS-R. Results: The experiments on the UCI Machine Learning datasets show that the proposed method classifies samples as correctly as M-CFIS-R with very lower run time (6.45 times on average). The experiments are performed through many tests via 2 main scenarios. Conclusion: The proposed system has good performance in reducing the computational time of inference with acceptable accuracy. It has ability to work with systems having limited knowledge and rule base.

Although bruxism is a common issue with a high prevalence, there has been a lack of epidemiological data about bruxism in Vietnam. This cross-sectional study aimed to determine the prevalence and associated factors of bruxism and its impact on oral health-related quality of life among Vietnamese medical students. Bruxism was assessed by the Bruxism Assessment Questionnaire. Temporomandibular disorders were clinically examined followed by the Diagnostic Criteria for Temporomandibular Disorders Axis I. Perceived stress, educational stress, and oral health-related quality of life were assessed using the Vietnamese version of Perceived Stress Scale 10, the Vietnamese version of the Educational Stress Scale for Adolescents, and the Vietnamese version of the 14-item Oral Health Impact Profile, respectively. The prevalence of bruxism, sleep bruxism, awake bruxism, and both conditions in Vietnamese medical students were 51.2%, 38.2%, 23.4%, and 10.4% respectively. Stress, temporomandibular joint pain, masticatory muscle pain, and tooth attrition were associated with the presence of bruxism. Vietnamese medical students were negatively affected by bruxism in terms of oral health-related quality of life.

A skeletal Class III malocclusion with open bite tendency is considered very difficult to treat orthodontically without surgery. This case report describes the lingual orthodontic treatment of an adult skeletal Class III patient with mandibular deviation to the left side, lateral open bite, unilateral posterior crossbite, zero overbite and negative overjet. The lower incisors were already retroclined to compensate with the skeletal discrepancy. The patient was treated by asymmetric molar extraction in the mandibular arch to retract the lower incisors and correct the dental midline, with the help of intermaxillary elastics. Lingual appliance was used with over-torqued lower anterior teeth’s brackets to control the torque of mandibular incisors. After a 30-month treatment, satisfactory smile and facial esthetics and good occlusion was achieved. A 12-month follow-up confirmed that the outcome was stable. Asymmetric molar extraction could be a viable option to retract mandibular incisors in Class III malocclusion with lower dental midline deviation.