Sachin C. Sarode

BACKGROUND/PURPOSE: Artificial intelligence (AI) has made deep inroads into dentistry in the last few years. The aim of this systematic review was to identify the development of AI applications that are widely employed in dentistry and evaluate their performance in terms of diagnosis, clinical decision-making, and predicting the prognosis of the treatment. MATERIALS AND METHODS: The literature for this paper was identified and selected by performing a thorough search in the electronic data bases like PubMed, Medline, Embase, Cochrane, Google scholar, Scopus, Web of science, and Saudi digital library published over the past two decades (January 2000-March 15, 2020).After applying inclusion and exclusion criteria, 43 articles were read in full and critically analyzed. Quality analysis was performed using QUADAS-2. RESULTS: AI technologies are widely implemented in a wide range of dentistry specialties. Most of the documented work is focused on AI models that rely on convolutional neural networks (CNNs) and artificial neural networks (ANNs). These AI models have been used in detection and diagnosis of dental caries, vertical root fractures, apical lesions, salivary gland diseases, maxillary sinusitis, maxillofacial cysts, cervical lymph nodes metastasis, osteoporosis, cancerous lesions, alveolar bone loss, predicting orthodontic extractions, need for orthodontic treatments, cephalometric analysis, age and gender determination. CONCLUSION: These studies indicate that the performance of an AI based automated system is excellent. They mimic the precision and accuracy of trained specialists, in some studies it was found that these systems were even able to outmatch dental specialists in terms of performance and accuracy.

BACKGROUND: The WHO Global Oral Health Action Plan has set an overarching global target of achieving a 10% reduction in the prevalence of oral conditions by 2030. Robust and up-to-date information on the global burden of oral conditions is paramount to monitor progress towards this target. The aim of this systematic data analysis was to produce global, WHO region, and country-level estimates of the prevalence of, and disability-adjusted life-years (DALYs) attributed to, untreated caries, severe periodontitis, edentulism, other oral disorders, lip and oral cavity cancer, and orofacial clefts from 1990 to 2021. METHODS: This report is based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. Input data were extracted from epidemiological surveys, population-based registries, and vital statistics. Data were modelled with DisMod-MR 2.1, a Bayesian meta-regression modelling tool, to ensure consistency between prevalence, incidence, remission, and mortality estimates for oral conditions. DALYs were estimated as the aggregation of the years of life lost (YLLs) due to premature mortality and years lived with disability (YLDs). YLDs were calculated by multiplying prevalence estimates, the severity of the oral condition's sequelae (disability weight) and duration of the sequelae. Although all oral conditions lead to YLDs, only lip and oral cavity cancer and orofacial clefts lead to YLLs as well. 95% uncertainty intervals (UIs) were generated for every metric with the 25th and 975th ordered 1000 draw values of the posterior distribution. FINDINGS: The combined global age-standardised prevalence of the main oral conditions (untreated caries, severe periodontitis, edentulism, and other oral disorders) was 45 900 (95% UI 42 300 to 49 800) per 100 000 population in 2021, with 3·69 billion (3·40 to 4·00) people affected globally. Untreated dental caries of permanent teeth and severe periodontitis were the most common oral conditions, with a global age-standardised prevalence of 27 500 (24 000 to 32 000) per 100 000 population and 12 500 (10 500 to 14 500) per 100 000 population, respectively. Edentulism, severe periodontitis, and lip and oral cavity cancer caused the highest burden as demonstrated by their counts of DALYs and age-standardised DALY rates. Existing trends for 1990-2021 reveal relatively small changes (upward or downward) in prevalence and burden. Increasing counts of prevalent cases and DALYs were noted for all oral conditions but untreated caries of deciduous teeth (no percentage change in prevalence or DALYs) and orofacial clefts (-68·3% [-79·3 to -46·5] decrease in DALYs). There were decreases in both age-standardised prevalence and DALY rate for untreated caries of permanent teeth and edentulism, no change in both for untreated caries of deciduous teeth and severe periodontitis, an increase in the prevalence but no change in the DALY rate for lip and oral cavity cancer, and no change in the prevalence but a decrease in the DALY rate for orofacial clefts. By WHO region, the African and Eastern Mediterranean regions showed the largest increases in prevalent cases and DALYs for most oral conditions, while the European region showed the smallest increases or no change. The European region was the only region with decreasing age-standardised prevalence of untreated caries in both deciduous (-9·88%; -12·6 to -6·71) and permanent teeth (-5·94% (-8·38 to -3·62). The prevalence and DALY rate of severe periodontitis decreased in the African region, while the prevalence and DALY rate of edentulism decreased in the African region, South-East Asia region, and Western Pacific region. Furthermore, DALY rates of lip and oral cavity cancer decreased in the European region and the region of the Americas, while DALY rates of orofacial clefts decreased in all regions. INTERPRETATION: The minor changes in the burden of oral conditions over the past 30 years demonstrate that past and current efforts to control oral conditions have not been successful and that different approaches are needed. Many countries now face the double challenge of controlling the occurrence of new cases of oral conditions and addressing the huge unmet need for oral health care. FUNDING: Bill & Melinda Gates Foundation.

BACKGROUND/PURPOSE: In the recent years artificial intelligence (AI) has revolutionized in the field of dentistry. The aim of this systematic review was to document the scope and performance of the artificial intelligence based models that have been widely used in orthodontic diagnosis, treatment planning, and predicting the prognosis. MATERIALS AND METHODS: The literature for this paper was identified and selected by performing a thorough search for articles in the electronic data bases like Pubmed, Medline, Embase, Cochrane, and Google scholar, Scopus and Web of science, Saudi digital library published over the past two decades (January 2000-February 2020). After applying the inclusion and exclusion criteria, 16 articles were read in full and critically analyzed. QUADAS-2 were adapted for quality analysis of the studies included. RESULTS: AI technology has been widely applied for identifying cephalometric landmarks, determining need for orthodontic extractions, determining the degree of maturation of the cervical vertebra, predicting the facial attractiveness after orthognathic surgery, predicting the need for orthodontic treatment, and orthodontic treatment planning. Most of these artificial intelligence models are based on either artificial neural networks (ANNs) or convolutional neural networks (CNNs). CONCLUSION: The results from these reported studies are suggesting that these automated systems have performed exceptionally well, with an accuracy and precision similar to the trained examiners. These systems can simplify the tasks and provide results in quick time which can save the dentist time and help the dentist to perform his duties more efficiently. These systems can be of great value in orthodontics.