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. 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 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%. 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.

Background: The aim of this study was to assess the salivary content of lipid peroxidation and antioxidants in patients with type 2 diabetes.

The aim of this review was to evaluate the most used suture materials with regards to their inflammatory response, their bacterial adhesion, and their physical properties when used to close oral wounds. Four databases (PubMed, Scopus, Dentistry & Oral Sciences, and OVID) were searched to retrieve relevant studies from January 1, 2000, to January 31, 2020. Out of the 269 articles, only 13 studies were selected as they were relevant and met the systematic review's protocol. These studies showed that almost all suture materials studies (catgut, polyglycolic acid [PGA] sutures, nylon, expanded polytetrafluoroethylene, and silk sutures) caused bacterial adherence and tissue reaction. In nylon and chromic catgut, the number of bacteria accumulated was lowest. Silk and nylon were found to be more impacted than catgut and PGA in terms of physical characteristics such as tensile strength. PGA, on the other hand, was said to be the most susceptible to knot unwinding. Following an oral surgical operation, all sutures revealed varied degrees of irritation and microbial accumulation. Nonresorbable monofilament synthetic sutures, however, exhibited less tissue response and less microbial accumulation.

Al-Rawi NH, et al. Imaging Sci Dent. 2019 Mar;49(1):45-51. https://doi.org/10.5624/isd.2019.49.1.45

Objectives: The aim of the study is to investigate the condylar position and its relation to articular eminence and axial condylar angle in temporomandibular joint disorder (TMD) patients and in normal controls using cone beam computed tomography (CBCT). Materials and Methods: CBCT temporomandibular joint (TMJ) images of 70 participants (38 males and 32 females, mean age 26.4 years) were analyzed. They were divided into control group (including 35 subjects) and study group (including 35 subjects). Linear measurements of joint space and condyle determined the condylar position of each TMJ. Articular eminence height and inclination were also measured with axial condylar angle to determine its relation to condylar position. Independent and paired sample t-test was applied to compare between the groups and TMJ sides of the same group at significance level of 0.05. Results: Statistical significant differences were found between males and females of both groups regarding superior joint space (SJS), lateral joint space, A-P, and M-L condyle distance (P < 0.05). SJS, medial joint space (MJS), and eminence angle were greater (P < 0.01) in male's joints with TMD with flatter axial condylar angle (P < 0.05), when compared with normal TMJ counterpart. Females TMJs showed significantly higher values of MJS of affected side when compared with normal counterpart with flatter axial condylar angle (P < 0.05). Conclusion: Superior and MJS parameters were the ones that showed significant differences between affected and nonaffected joints. The mean axial condylar angle was smaller in joints with abnormal TMJ. This indicates that the condyles of the affected joints may rotate inward.