Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.

Background: Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on risk factors, systematic medical condition, and clinic-pathological features.Methods: A popular data mining algorithm artificial neural network was used for developing the artificial intelligence-based prediction model. A total of 29 variables that were associated with the patients were used for developing the model. The dataset was randomly split into the training dataset 54 (75%) cases and testing dataset 19 (25%) cases. All records and observations were reviewed by Board-certified oral pathologist.Results: A total of 73 patients met the eligibility criteria. Twenty-two (30.13%) were benign cases, and 51 (69.86%) were malignant cases. Thirty-seven were female, and 36 were male, with a mean age of 63.09 years. Our analysis displayed that the average sensitivity and specificity of ANN for oral cancer prediction based on the 10-fold cross-validation analysis was 85.71% (95% confidence interval [CI], 57.19-98.22) and 60.00% (95% CI, 14.66-94.73), respectively. The accuracy of ANN for oral cancer prediction was 78.95% (95% CI, 54.43-931.95). Conclusion:Our results suggest that this machine-learning technique has the potential to help in oral cancer screening and diagnosis based on the datasets. The results demonstrate that the artificial neural network could perform well in estimating the probability of malignancy and improve the positive predictive value that could help to predict the individuals' risk of developing OC based on knowledge of their risk factors, systemic medical conditions, and clinic-pathological data. K E Y W O R D Sartificial neural network, early detection, machine learning, oral cancer, prediction model | INTRODUC TI ONOral cancer (OC) is the 11th most common cancer in the world, the estimated number of new cases of OC is around 657 000, which accounts for more than 330 000 deaths each year. 1 In Saudi Arabia, OC is the third most common malignancy, with lymphoma and leukemia at the first and second positions. 2 Oral squamous cell carcinoma is the most common type of OC. 3 The prevalence of oral cancer How to cite this article: Alhazmi A, Alhazmi Y, Makrami A, et al. Application of artificial intelligence and machine learning for prediction of oral cancer risk.

Background Tinospora cordifolia (Thunb.) Miers (Giloy) has been applied successfully as an anti-inflammatory, anti-diabetic, and even as an anti-cancer agent. Yet, to date, the application of Giloy has not been explored concerning oral cancer. Objectives To assess the effect of T cordifolia (Thunb.) Miers (Giloy) extract (TcE) on an oral cancer cell line. Methods AW13516 (oral cancer cell line) cells were treated with the prepared aqueous extract of TcE for 24 h at various concentrations ranging between 5 μg/ml and 100 μg/ml and compared with control (cells without treatment). Thee effect of the extracts on apoptosis was assessed by through Annexin V flow cytometry assay and Luminometry based assessment of Caspase 8, 9 and caspase 3/7 activity. RNA was isolated from treated cells and gene expression of selected metastatic genes (MMP1, MMP10, and CXCL8); epithelial-mesenchymal stem cell genes (TWIST1, SNAIL, ZEB1, Oct4) and stemness related genses (Nanog, Sox2) were analyzed by using a quantitative real-time PCR system. The experiments were performed in triplicates. Results Aqueous extract of TcE was found to induce apoptosis inducer in AW13516 cells in a concentration-dependent manner and was potent even at a low concentration of 5 μg/ml. The apoptosis induction was confirmed with the caspase activity assay. Treatment of the cells with the extract for 24 h exhibited a significant decrease in the expression of EMT genes in a dose-dependent manner without an effect on the metastatic genes. Conclusion Aqueous extract of TcE induces apoptosis-mediated cell death in the oral cancer cell line AW13516 while attenuating its potential for epithelial mesenchymal transition.

(1) Objective: To review the criteria proposed by Cerero-Lapiedra et al. and to retrospectively identify the under-diagnosed disease in patients diagnosed with proliferative verrucous leukoplakia. (2) Materials and methods: In this study, we included patients who were diagnosed with leukoplakia (histological label consistent with the clinical diagnosis, n = 95), and cases with a final diagnosis within the spectrum of proliferative verrucous leukoplakia (n = 110) as defined by Batsakis et al. We applied the criteria proposed by Cerero-Lepiedra et al. to screen for the possible cases of proliferative verrucous leukoplakia. (3) Results: Although many of our patients satisfied specific isolated criteria, only 11 cases satisfied specific combinations of the guidelines to satisfy a diagnosis of proliferative verrucous leukoplakia. However, due to the lack of follow-up data, the disease is not confirmed in these 11 cases. (4) Conclusion: A limited number of cases of proliferative verrucous leukoplakia were diagnosed using the criteria given by Cerero-Lapiedra et al. The true natural history of the disease could not be studied due to the lack of follow-up data. (5) Clinical relevance: Proliferative verrucous leukoplakia presenting as hyperkeratosis or mild epithelial dysplasia are often not followed up, and they subsequently transform into carcinoma. Thus, clinicians must be vigilant whenever they encounter leukoplakia, especially with multifocal presentations. In such cases, the follow-up data are the key to understanding the true nature of the disease entity.

The objective of the present article was to qualitatively and quantitatively review the association between chronic mechanical irritation and oral squamous cell carcinoma. PubMed, SCOPUS, and Web of Science databases were searched using the keyword combinations ‘chronic trauma and oral squamous cell carcinoma; chronic irritation and oral squamous cell carcinoma; chronic irritation and oral cancer; chronic trauma and oral cancer.’ Duplicates and irrelevant articles were excluded after the title and abstract screening. The full texts of the remaining articles were assessed using selection criteria. A total of 375 (PubMed-126; SCOPUS-152; WOS-97) articles were screened, and 343 duplicates and irrelevant articles were excluded. Only 9 of the remaining 32 articles met the selection criteria and were included in the qualitative analysis. Buccal mucosa and tongue, being highly prone to chronic irritation through the dental prosthesis, were the common sites for oral squamous cell carcinoma. Edentulous subjects with ill-fitting dentures were at a high risk of developing chronic irritation associated-oral squamous cell carcinoma. According to the Joanna Briggs Institute of risk assessment, eight of the nine included studies had a low risk of bias. The quantitative analysis showed a significant association (p<0.00001) between the chronic oral mucosal irritation and oral squamous cell carcinoma with an overall risk ratio of 2.56 at a confidence interval of 1.96 to 3.35. Chronic oral mucosa irritation has a significant association with oral squamous cell carcinoma, and the nature of association could be that of a potential co-factor (dependent risk factor) rather than an independent risk factor.