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.

Aim: The aim of this study is to evaluate the enamel remineralization after treatment with three different remineralizing agents using surface microhardness assessment. Materials and Methods: This in vitro study involves 50 enamel samples divided into five groups of 10 samples each. The positive control group consisted of intact enamel and a negative control group consisted of demineralized enamel samples. All groups excluding the positive control group were subjected to demineralization following which three of these groups were remineralized using remineralizing agents (casein phosphopeptide amorphous calcium phosphate [CPP-ACP] [GC tooth mousse], casein phosphopeptide amorphous calcium phosphate with fluoride [CPP-ACPF] [GC tooth mousse plus], sodium fluoride [phos-flur]). The groups treated with remineralizing agents were subjected to pH cycling over a period of 28 days. This was followed with assessment of surface microhardness (Micro Vickers Hardness tester, Matsuzawa Co., Ltd, Toshima, Japan). Statistical Analysis: One-way analysis of variance test and posthoc Tukey test were conducted for multiple group comparison. Results: There was an improved enamel remineralization in the group, remineralized using CPP-ACPF in comparison with the other groups. Conclusion: Casein phosphopeptide with fluoride is a promising material for remineralization of enamel subsurface lesions.

Drogenase levels. Therefore, a study was carried out to determine the changes in the salivary alterations in lactate dehydrogenase (LD) levels oral leukoplakia (OL) and oral cancer (OC). Methods: Seventy-five patients reporting to department of oral medicine and radiology, were enrolled into the study which includes 25 patients each of oral leukoplakia, 25 of oral cancer and 25 healthy controls (HC). Unstimulated whole saliva measuring 1 mL will be collected from each of these patients by spit method in centrifuged and evaluated for LDH levels using the standard kit method. The data obtained were subjected to statistical analysis using the SPSS software version 17. Results: The mean salivary lactate dehydrogenase levels were higher in males in comparison to females in all three study groups OL, OC and HC. The salivary lactate dehydrogenase levels in the controls group, oral leukoplakia group and oral cancer were 79.50 ± 4.67 IU/L, 136.46 ± 3.36 IU/L and 148.77 ± 4.83 IU/L, respectively. There was a significant difference in the mean salivary levels of the above groups. Conclusion: Salivary lactate dehydrogenase levels are consistently higher in oral precancer and cancer hence it could be future marker...

The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans. Articles were retrieved from MEDLINE, Cochrane CENTRAL, IEEE Xplore, and Google Scholar. Eligible studies were analyzed thematically, and their quality was appraised using the JBI checklist for diagnostic test accuracy studies. Meta-analysis was conducted for key performance metrics, including Dice Similarity Coefficient (DSC) and Average Surface Distance (ASD). A total of 767 non-duplicate articles were identified, and 30 studies were included in the review. Of these, 27 employed deep-learning models, while 3 utilized classical machine-learning approaches. The pooled DSC for mandible segmentation was 0.94 (95% CI: 0.91–0.98), mandibular canal segmentation was 0.694 (95% CI: 0.551–0.838), maxilla segmentation was 0.907 (95% CI: 0.867–0.948), and teeth segmentation was 0.925 (95% CI: 0.891–0.959). Pooled ASD values were 0.534 mm (95% CI: 0.366–0.703) for the mandibular canal, 0.468 mm (95% CI: 0.295–0.641) for the maxilla, and 0.189 mm (95% CI: 0.043–0.335) for teeth. Other metrics, such as sensitivity and precision, were variably reported, with sensitivity exceeding 90% across studies. AI-based segmentation, particularly using deep-learning models, demonstrates high accuracy in the segmentation of dental and maxillofacial structures, comparable to expert manual segmentation. The integration of AI into clinical workflows offers not only accuracy but also substantial time savings, positioning it as a promising tool for automated dental imaging.

Introduction : With better health care facilities and nutritional levels the average life expectancy of Indian population has been on the rise over the years. Most of the geriatric population is under long-term medication. Aim : The aim of this study was to evaluate the synergistic effect of multiple xerostomia drugs. Materials and Methods : Unstimulated saliva was measured in 60 geriatric patients, and xerostomia questionnaire and quality-of-life scale were also administered. Results : There was a very highly significant reduction in the salivary flow rates of patients under multiple xerostomia-inducing drugs. Conclusion : The synergistic effect of the xerostomia inducing medication could be the possible factor responsible for reduced salivary flow in elderly individuals using such drugs