Introduction: Down syndrome is present in every 1 in 700 births, associated with developmental disability and comorbidities, like obesity which significantly contributes to morbidity and mortality in these children. Objective: To identify the trends in body mass index and prevalence of obesity in school going down syndromes. Methodology: A descriptive cross-sectional survey conducted at special children’s schools in Islamabad and Rawalpindi. A questionnaire was designed after literature search and was filled by the parents of the selected candidates according to inclusion criteria i.e., school going down syndromes and residents of Islamabad and Rawalpindi, in an interactive counselling session conducted by authors in collaboration with pediatrician, with parents at respective schools of their child, keeping the questionnaire anonymous. After data collection it was entered in SPSS v25 for statistical analysis in terms of frequencies and percentages. Results: Our survey included 44 candidates in which majority was between the age group 11 years to 20 years i.e., 52% (n=23) and 27% (n=12) were below 10 years. This survey showed that 95% (n=42) of our selected candidates have Normal BMI while 4.5% (n=2) of our candidates fall in Overweight (High BMI) category. Conclusion: Most of our selected Down syndrome individuals are protected from obesity as they indulge in different sports, physical activities, physiotherapy and maintain a balanced diet. It is therefore recommended to emphasize the importance of physical activities, active participation in sports, gymnastics, regular exercises and maintaining a healthy diet plan to avoid obesity in these individuals so that further complications of obesity can be prevented thus improving the quality of life of these individuals. Keywords: Body Mass Index, Down Syndrome, Obesity

Background: Down syndrome is present in every 1 in 700 births, associated with developmental disability and comorbidities, like obesity which significantly contributes to morbidity and mortality in these children. Aim: To identify the trends in body mass index and prevalence of obesity in school going down syndromes. Methodology: A descriptive cross-sectional survey conducted at special children’s schools in Islamabad and Rawalpindi. A questionnaire was designed after literature search and was filled by the parents of the selected candidates according to inclusion criteria i.e., school going down syndromes and residents of Islamabad and Rawalpindi, in an interactive counselling session conducted by authors in collaboration with pediatrician, with parents at respective schools of their child, keeping the questionnaire anonymous. After data collection it was entered in SPSS v25 for statistical analysis in terms of frequencies and percentages. Results: Our survey included 44 candidates in which majority was between the age group 11 years to 20 years i.e., 23(52%) and 12(27%) were below 10 years. This survey showed that 42(95%) of our selected candidates have Normal BMI while 2(4.5%) of our candidates fall in Overweight (High BMI) category. Conclusion: Most of our selected Down syndrome individuals are protected from obesity as they indulge in different sports, physical activities, physiotherapy and maintain a balanced diet. It is therefore recommended to emphasize the importance of physical activities, active participation in sports, gymnastics, regular exercises and maintaining a healthy diet plan to avoid obesity in these individuals so that further complications of obesity can be prevented thus improving the quality of life of these individuals. Keywords: Body Mass Index, Down Syndrome, Obesity

Artificial Intelligence has exploded as a research subject in the 21st century as the hardware requirements of theoretical Artificial intelligence in the 19th and 20th century have translated into reality. This has led to rapid progress with the realization of multiple kinds of neural networks, as well as improvements along classical Machine Learning models like Decision Trees, Support Vector Machines etc. Most recently, the focus in Machine Learning has now shifted towards generative networks with Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) leading modern research topics. With advent of the Internet of Medical Things (IoMT) and modern time leaps in the domain of Artificial Intelligence with the explosion of Machine and Deep Learning, Medical AI has grasped the central stage in modern research. Applications of Medical AI vary from detections of various cancerous tumors to prediction of arrhythmia attacks. The augmentation of such AI embedded techniques into the medical profession has streamlined analysis as well as aided professionals in reaching a more efficient and accurate diagnosis. This has also translated in the field of dentistry where strong deep learning architectures such as Convolutional neural networks in the form of Resnet, Inception, GoogleNet etc have been used to process raw images and detect a range of dental diseases. This paper aims to provide an overview of progress made in the field of Machine Learning particularly focusing on its medical and dental applications. Different facets of Machine Learning are discussed with respect to their strengths, shortcomings, and the way artificial intelligence has been used to tackle problems in the medical field. Finally, a descriptive overview about state-of-the-art machine learning reliant applications that are being used in different dental subfields is discussed along with current challenges the industry faces today.

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Background: Down syndrome or trisomy 21, is mostly associated with characteristic facial features, delayed physical growth and mild to moderate intellectual disability with co-morbidities. Not many people are well aware of facts related to down syndromes. Aim: To identify the level of awareness and knowledge among parents of school going down syndrome. Methodology: A descriptive cross-sectional survey conducted at special children’s schools in Islamabad and Rawalpindi, from February 2021 to March 2021. Sample size was 44. We adopted convenience non-probability sampling technique. Parents that fulfill the inclusion criteria i.e. school going down syndromes, 5 to 32 years of age and residents of Islamabad and Rawalpindi, in an interactive counselling session, keeping the questionnaire anonymous. After data collection it was entered in SPSS v25 for statistical analysis. Results: Analysis of the questionnaire regarding the Knowledge of physical features of a child with Down syndrome showed that most 97% of the people were aware about facial profile and nose. 65.9% people responded positively regarding their knowledge about small head, ears and nose. 88.6% had knowledge about wide, short hands and short fingers. 65.9 knew about single deep crease across palm. 88.6 told that Down’s children had long tongue. 45.5% knew they have squint. 88.6% have knowledge about loss of muscle tone. Regarding memory 45.5% knew they have poor memory while 93.2% knew they are slow learners. Conclusion: Our selected candidates are mostly well aware of this condition but there is still the need for spreading further awareness to overcome the problems faced by the child and for their better management. Keywords: Awareness, Down Syndrome, Communication