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Background Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from “WebCeph”™. Methods Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to “WebCeph”™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained. Results All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90. Conclusion A good agreement was found between the cephalometric measurements obtained from “WebCeph”™ and manual tracing.

Supernumerary tooth/hyperdontia is defined as those teeth which are present in excess of the usual distribution of twenty deciduous and thirty-two permanent teeth. It can be seen in both syndromic and nonsyndromic patients. In Nepalese population, prevalence of supernumerary tooth is documented to be 1.6%. To the best of our knowledge, no studies from Nepal have reported the incidence of bilateral maxillary paramolars or the combination of unilateral maxillary paramolar and distomolar till date. Hence, we are reporting these two cases with a brief review of literature to put emphasis on incidence, prevalence, proposed hypothesis for etiology, and management of supernumerary teeth.

Considering the widespread transmission of Coronavirus disease (COVID-19) globally, India is also facing the same crisis. As India already has inadequate waste treatment facilities, and the sudden outbreak of the COVID-19 virus has led to significant growth of Bio-medical waste (BMW), consequently safe disposal of a large quantity of waste has become a more serious concern. This study provides a comprehensive assessment of BMW of India before and during the COVID-19 pandemic. Additionally, this article highlights the gaps in the implementation of BMW rules in India. This study uses various government and non-government organizations, reports and data specifically from the Central Pollution Control Board (CPCB). The finding of the study demonstrated that most of the States/Union Territories (UTs) of India are lacking in terms of COVID-19 waste management. India has generated over 32,996 mt of COVID-19 waste between June and December 2020. During this period, Maharashtra (789.99 mt/month) is highest average generator of COVID-19 waste, followed by Kerala (459.86 mt/month), Gujarat (434.87 mt/month), Tamil Nadu (427.23 mt/month), Uttar Pradesh (371.39 mt/month), Delhi (358.83 mt/month) and West Bengal (303.15 mt/month), and others respectively. We draw attention to the fact that many gaps were identified with compliance of BMW management rules. For example, out of all 35 States/UTs, health care facilitates (HCFs), only eight states received authorization as per BMW management rules. Moreover, the government strictly restricted the practice of deep burials; however, 23 States/UTs are still using the deep burial methods for BMW disposal. The present research suggests that those States/UTs generated on an average of 100 mt/month COVID-19 waste in the last 7 months (June–December 2020) should be considered as a high priority state. These states need special attention to implement BMW rules and should upgrade their BMW treatment capacity.