Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on different...

Advancements in the domain of computer science have made artificial intelligence (AI) almost ubiquitous in everyday life. Dentistry is known to easily adapt to new technologies and hence can easily acclimatize to this nascent field. With AI spreading into dentistry, models are being used to ascertain almost every dental condition, ranging from the routine dental caries to the more complex conditions like oral cancer, maxillofacial cysts, alveolar bone loss, and even appraising the urgency for orthodontic extractions. Artificial intelligence, has untapped potential and shows promising prospects. Multiple studies have been evaluated to highlight the progress made till date and it has been seen that AI based automated systems are exceptional in a limited domain and perform on par to dental specialists on a variety of performance parameters. Better adaptation and utilization of technology will help in better and precise treatment outcomes, while also reducing the work burden of the clin...

Although dated back to 1950, artificial Intelligence (AI) has not become a practical tool until two decades ago. In fact, AI is the capacity of machines to do tasks that normally require human intelligence. AI applications have been started to provide convenience to people's lives due to the rapid development of big data computational power, as well as AI algorithm. Furthermore, AI has been used in every dental specialties. Most of the applications of AI in dentistry are in diagnosis based on X-ray or visual images, whereas other functions are not as operative as image-based functions mainly due to data availability issues, data uniformity and computing power for processing 3D data. AI machine learning (ML) patterns assimilate from human expertise whereas Evidence-based dentistry (EBD) is the high standard for the decision-making of dentists. Thus, ML can be used as a new precious implement to aid dental executives in manifold phases of work. It is a necessity that institutions integrate AI into their theoretical and practical training programs without forgetting the continuous training of former dentists.

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD)...

The study of monuments of monetary circulation of the Russian Middle Ages has significantly intensified in the last decade, having acquired a previously inaccessible technical base. Numismatics is acquiring the features of a more exact science, using the tools of scrupulous graphical reconstruction of stamps, restoring technological connections, studying the metrology of coins that are becoming available for research in increasing quantities, and applying mathematical models to the technical characteristics of ancient monuments. The article deals with the methodology of studying the monuments of numismatics and «small» sphragistics of Ancient Rus’.

Down syndrome (DS) is defined as trisomy chromosome 21, which is observed to be the most prevalent autosomal genetic anomaly. Salivary immunoglobulin A (sIgA) plays an important role in defence mechanism against the microbial pathogens that can invade the surface of mucosa. In children with DS, raised sIgA levels cause an increase in immune mechanism against dental caries. The evidence about the salivary IgA levels in children suffering from Down syndrome is contradictory and limited in the literature. Thus, there is a terrible requirement to conduct more longitudinal prospective studies on saliva of DS patients taking large sample sizes to assess the molecular interactions. The present review is aimed to evaluate the association of Dental caries and salivary immunoglobulin A in Down syndrome individuals. The sources were searched from a database of PubMed from 2004 to 2023 (last 19 years). The inclusion criteria included key words of microdontia, delayed eruption, dental anomalies, retained teeth, anodontia, fissured lip and tongue, macroglossia, hypodontia in Down syndrome. The 22 articles that met the selection criteria (most of them were review article and some observational studies) were included to this review, of which some of the studies have been conducted to assess the role of sIgA in relation to dental caries and observed that increased sIgA levels decrease the rate of incidence of dental caries. The present review revealed that patients suffering with Down syndrome are at an increased risk of dental caries because of impaired motor activities, malocclusion and affected quality of life.

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