COVID‐19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Recently, COVID‐19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID‐19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID‐19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID‐19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID‐19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID‐19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID‐19 and inspire their works for future development.

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Introduction: Special needs children experience poorer oral health when compared to their compatriots. Moreover, Special Care School Children (SCSC) experience significant barriers to access proper oral health care. It has been found that they have high unmet oral health needs. Aim: The aim of this study was to assess the effectiveness of preventive oral health measures in a group of SCSC; boys, in Al-Kharj Governorate of Saudi Arabia. Materials and Methods: A longitudinal study was designed among SCSC in Al-Kharj Governorate of Saudi Arabia starting from October 2016 for a period of eight months.Only schools for boys were included in the study. Out of 936 students enrolled in the special needs education programme in 18 boys schools; 163 SCSC (boys) from eight primary schools were eventually included in the study. After the base line survey; the subjects were evaluated with predefined criteria at three monthly intervals until six months. The effectiveness of various preventive measures was evaluated for reduction in the risk of oral diseases. SCSC were divided into groups as per their specific health care need. Preventive oral health measures such as supervised tooth brushing with a fluoridated tooth paste was introduced with the help of teachers and parents of SCSC. Plaque levels were assessed by means of Plaque index. Various indices were used to measure dental caries including Decayed, Missing, and Filled Teeth (DMFT/dmft) as well as Decayed, Missing, and Filled Surfaces (DMFS/dmfs) index. The risk for dental caries was assessed by means of a cariogram model at the start and at the end of campaign. The data was computed using SPSS v20 programme. Means of the overall plaque score and the caries indices scores were calculated and compared among various special need groups among the SCSC. The significance level was set at p<0.05. Results: The overall mean plaque score of the group was 1.55. Plaque scores and mean decayed (D) component were significantly higher in intellectual disabilities as compared to physical disabilities. The mean DMFT and DMFS score was 3.2 and 6.42, respectively with mean decayed (D) component score of 2.67. There was no significant difference among caries prevalence and decayed (D) component among various groups of disabilities. Plaque index score reduced to 1.35 after three months and finally to 1.1 after six months. This was statistically significant (p<0.05). The actual chance to avoid new cavities in the cariogram increased from 5% to 73% at the end, for the SCSC boys. Conclusion: There was a significant decrease in the risk for oral diseases after incorporation of preventive oral health measures for SCSC.