Contemporary Role and Applications of Artificial Intelligence in Dentistry.

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

Impacts of severity of Covid-19 infection on the morbidity and mortality of surgical patients.

Introduction: One of the challenges of surgery on patients with active SARS-CoV-2(severe acute respiratory syndrome coronavirus 2) infection is the increased risk of postoperative morbidity and mortality. Aim: This study will describe and compare the postoperative morbidity and mortality in asymptomatic patients or those with mild infection with those with severe COVID-19 infection undergoing elective or and emergency surgery. Materials and methods: This is a retrospective study of 37 COVID19 patients who had the infection 7 days prior to and 30 days after emergency or elective surgery. Patients were divided to two groups. Group1: the asymptomatic or those with mild infection that is diagnosed just before surgery (14 patients). Group 2: those who were admitted to the hospital because of severe COVID-19 and were operated for COVID-19 related complications (23 patients). Morbidity and mortality of both groups was studied. Results: There was no significant difference in gender between the two groups. There were 5 females (2 in group 1, and 3 in group 2) and 32 males (12 in group 1, and 20 in group 2). Mean age for all patients was 49.8years (38 for group 1 and 57 for group2). Median age for all patients was 50 years (37.5 for group 1 and 57 years for group 2). Sepsis developed in 7 patients (1 patient in group 1 and in 6 patients in group 2). Statistically there was no significant difference in occurrence of sepsis between the two groups. There was a significant difference in the intensive care stay between the two groups (higher in group 2). Four deaths were reported in group 1 and fourteen in group 2. Eighteen out of thirty-seven patients died. Conclusion: Severity of COVID-19 infection will prolong the hospitalization and ICU stay in surgical patients with no significant effect on mortality.

The Effectiveness of Artificial Intelligence in Detection of Oral Cancer.

AIM: The early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. MATERIALS AND METHODS: From the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%. RESULTS: Results obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%. CONCLUSIONS: There is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.