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Background: Artificial intelligence (AI) based on deep learning (DL) algorithms has shown promise in enhancing the speed and accuracy of dental anomaly detection in paediatric dentistry. Aim: This systematic review aimed to investigate the performance of AI systems in identifying dental anomalies in paediatric dentistry and compare it with human performance. Design: A systematic search of Scopus, PubMed and Google Scholar was conducted from 2012 to 2022. Inclusion criteria were based on problem/patient/population, intervention/indicator, comparison and outcome scheme and specific keywords related to AI, DL, paediatric dentistry, dental anomalies, supernumerary and mesiodens. Six of 3918 initial pool articles were included, assessing nine DL sub‐systems that used panoramic radiographs or cone‐beam computed tomography. Article quality was assessed using QUADAS‐2. Results: Artificial intelligence systems based on DL algorithms showed promising potential in enhancing the speed and accuracy of dental anomaly detection, with an average of 85.38% accuracy and 86.61% sensitivity. Human performance, however, outperformed AI systems, achieving 95% accuracy and 99% sensitivity. Limitations included a limited number of articles and data heterogeneity. Conclusion: The potential of AI systems employing DL algorithms is highlighted in detecting dental anomalies in paediatric dentistry. Further research is needed to address limitations, explore additional anomalies and establish the broader applicability of AI in paediatric dentistry. [ABSTRACT FROM AUTHOR]

Dentists face a higher risk of contracting the coronavirus due to the nature of dental procedures, which involve comprehensive examinations, diagnoses, and therapeutic interventions. The objective of this systematic review was to assess the impact of teledentistry on oral health promotion and prevention, with the aim of enhancing oral health compared to conventional strategies, regardless of patient age. The research methodology involved a systematic review following the PRISMA guidelines, and the review protocol was registered with PROSPERO. A comprehensive literature search was conducted in PubMed, ScienceDirect, and Ebscohost databases, covering the period from 2018 to 2022. The initial search yielded 256 potentially relevant published articles, which were screened for duplicates. After removing the duplicates, 145 titles were considered for further evaluation. After scrutinizing the titles and abstracts, 120 studies were found to be ineligible according to our criteria, leaving 4 studies to be assessed at the full-text level. The findings of this review indicate that teledentistry, particularly through the use of messages or apps, primarily benefits younger populations by significantly reducing dental biofilm stagnation, gingivitis, and the plaque index. Consequently, evidence suggests that teledentistry is an effective approach for improving oral health among children and adolescents. [ABSTRACT FROM AUTHOR]

Dental caries is a significant public health issue, especially in children, with high prevalence rates that highlight the necessity for effective detection and management strategies. Current diagnostic methods vary in consistency, prompting the integration of Artificial Intelligence (AI) to enhance reliability and standardization. This study aims to assess the inter-rater reliability (IRR) in dental caries detection among dental students using traditional and AI-enhanced calibration methods, preparing them for accurate assessments in clinical and educational settings. Eight fifth-year dental students participated in a structured calibration program. Calibration effectiveness was measured using Cohen's Kappa for IRR, and assessments were conducted across two sessions to evaluate the consistency and reliability of the examiners. The calibration showed an average agreement level of 69.87% for slide- based methods and 74% for dental phantoms. Kappa values ranged from 0.61 to 0.90, suggesting substantial to almost perfect agreement. AI applications demonstrated the potential to streamline and enhance the diagnostic process. The study successfully bridged theoretical knowledge and practical skills, showing that dental phantoms combined with AI applications can significantly improve the accuracy and consistency of dental caries assessments. This calibration approach enhances diagnostic skills and facilitates the integration of advanced technologies in dental education and practice. [ABSTRACT FROM AUTHOR]

Big data is growing fast following Big data has the characteristics of high volume, speed, and variety (3v) and continues to grow exponentially following the development of the world's use of information and communication technology. The main problem with big data is the data deluge. The need for technology and large data storage and processing methods to keep pace with the exponential data growth rate is potentially limitless, giving rise to the problem of exponentially increasing technology requirements as well. The weakness of previous big data analysis approaches (batch and online real time processing) is that it requires high technology (large storage, memory and processing). This paper proposes a new approach in big-data analysis by separating the basic knowledge (BK) construction process from original data with a much smaller volume (volume reduction), then analyzing it into final knowledge, thus requiring smaller/simpler analysis technology. The proposals include formulating the definition and representation of BK, developing methods for constructing BK from source data, and analyzing BK into final knowledge. We propose a BK construction method based on a knowledge extraction technique using BIRCH clustering algorithm for instance reduction, and handling high-dimensional problems by parallelizing the dimension calculation process to calculate the distance between instances. We use the Adjusted Rand Index (ARI) to measure the similarity of the final knowledge of the baseline and proposed methods. First, the BIRCH baseline was modified by parallelizing the calculations has succeeded in increasing speed from 17% to 25%. Next, the parallel BIRCH (PBIRCH) baseline was broken into BK construction and BK analysis, has succeeded in reducing volume by 96% or more and increasing speed by 43.50%, with similar final knowledge results (ARI=1). Based on these results, we conclude that the BK construction method and analysis from BK into final knowledge for highdimensional big data have significantly reduced volume and speed up the analytical process without reducing the quality of the final knowledge. [ABSTRACT FROM AUTHOR]

Application of the recommender system on the online platform turns out to produce a large volume of data and at an unexpected rate, making it more realistic to study the recommender system under streaming settings. Many studies have tried to develop a streaming recommender system, one of which is Stream-centered Probabilistic Matrix Factorization (SPMF). In SPMF, model will be updated continuously every time a data stream window enters the system using the data managed in reservoir plus the data in data stream window. This study focuses on developing a reservoir sampling technique in SPMF method, namely uniform random sampling, which can result in model ranking errors due to the loss of observational data from memory. To reduce this consequence, sample is selected based on the matrix factorization score, which is considered to represent the popularity of item. In addition, this research also developed a parallelization technique for the updating of the SPMF algorithm for the purpose of increasing scalability. Based on the experiments conducted on E-commerce dataset, the proposed method is proven to have better recommendation quality than the baseline method and reaches at least 84% quality of BPR-MF with a reduction of the dataset up to 78% and computation time 60 times faster. Parallel computing can also increase computation speed by 23% by using 4 workers. [ABSTRACT FROM AUTHOR]

Ontology alignment is a collection of correspondences or mappings between entities from two ontologies being matched. Alignment is an essential data resource that used as a reference to build interlinking on Linked Data. Since it is produced by an ontology matching system, the good quality of alignment determines the good performance of the ontology matching system. The coherence of alignment is one of the success criteria of the ontology matching system so that some systems implement the alignment repair feature in it. This feature restores the incoherent to coherent by removing some mappings in alignment. Research of alignment incoherence stated that the repair process should have a minimum impact on alignment by minimizing the number of removed mappings and/or minimizing the confidence value of removed mappings. The study discussed in this paper is alignment repair to restore coherent conditions with two minimization focus of diagnosis. In a repair system, the process of finding and removing a mapping to produce conflict-free alignment is called the diagnosis. We have been proven that conflict-free alignment is coherent alignment. The proposed repair system implements a dynamic weighting heuristic to guide the search for minimum removed mapping with two minimization focus, in order to produce conflict-free alignment. Experiment on 8 alignments shows that the current system which removes more mapping will produce conflict-free alignment, but does not support minimal impact. Conversely, the current system that removes minimal mapping does not support conflict-free alignment. The proposed system excels at producing conflict-free alignment with minimal impact. [ABSTRACT FROM AUTHOR]