Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.

OBJECTIVE: This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD: Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS: After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS: AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE: Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.

Evaluation of the current MicroRNAs expression levels as potential biomarkers in Oral Squamous Cell Carcinoma.

As the most common malignancy, oral squamous cell carcinoma (OSCC) is typically fatal. The survival of patients with oral cancer has not improved, and tumor recurrence remains high. During tumorigenesis, microRNAs (miRNAs) regulate gene expression. Patients' life expectancy can be determined by prognostic survival biomarkers, which can focus therapy on specific targets. This study evaluated five miRNAs associated with OSCC for their prognostic impact. It was determined through microarray analysis and quantitative reverse transcription polymerase chain reaction that there was a significant difference in the expression of miRNAs between OSCC patients and control patients in plasma. We used the unpaired t-tests and the Mann-Whitney test to conduct the statistical analysis. Based on the study's results, five miRNAs have been found to have significantly different expression levels in the plasma of patients with OSCC; in particular, miR-31 was found to have a significantly higher expression level in OSCC patients' plasma as compared with healthy controls. Aside from that, there was a significant reduction in the expression of miR-100, miR-199a, miR-203, and mir345 in the plasma of OSCC patients (P < 0.05). To better understand the importance of miRNAs in OSCC, various OSCC cases were analyzed. Detecting miRNAs in plasma may be a useful diagnostic tool for oral squamous cell carcinoma.

The current novel drug delivery system (natural and chemical composites) in dental infections for antibiotics resistance: a narrative review.

A mouth infection can also affect the teeth, the mouth tissues, and any other areas involved in the mouth. Biofilms formed by bacteria are the primary cause of mouth infections and other infectious diseases caused by bacteria. The most common dental problem is an infection or disease within the mouth. The term chronic infection is sometimes used to describe this type of problem. There is also the possibility that these discomforts may occur due to the presence of bacteria in plaque, which is responsible for causing inflammation throughout the body as a result of bacterial infection in the mouth. In many cases, antibiotics serve as a first-line treatment for mouth infections, especially those caused by bacteria, most commonly treated by antibiotics. It is common for antibiotics to be used orally, and they are absorbed into the body through their metabolism in the liver and kidneys. Antibiotic resistance, which is primarily caused by misuse and overuse of antibiotics, is also one of the most significant public health crises of the 21st century. With the help of new drug delivery systems, antibacterial resistance can be decreased in humans to maintain the effectiveness of antibiotics when they are used more frequently. By directly delivering antibiotics to damaged tissues and reducing undesirable side effects when administered systemically, antibiotic delivery systems enhance the efficiency of antibiotics in specific zones. Furthermore, several new delivery systems are being explored in an attempt to improve pharmacokinetics and pharmacodynamics, reduce bacterial resistance, and decrease dose times. As a result, antibiotics were delivered to tissues and biological fluids using an innovative delivery system. Research on some of the most prevalent dental diseases provides updates on antibiotic delivery systems that reduce antibiotic resistance. This review overviews oral infectious diseases, antibiotics effects, and the different delivery systems of these therapeutic approaches.