Introduction: Oral squamous cell carcinoma (OSCC) presents a significant global health challenge. The integration of artificial intelligence (AI) and computer vision holds promise for the early detection of OSCC through the analysis of digitized oral photographs. This literature review explores the landscape of AI‐driven OSCC automatic detection, assessing both the performance and limitations of the current state of the art.Materials and Methods: An electronic search using several data base was conducted, and a systematic review performed in accordance with PRISMA guidelines (CRD42023441416).Results: Several studies have demonstrated remarkable results for this task, consistently achieving sensitivity rates exceeding 85% and accuracy rates surpassing 90%, often encompassing around 1000 images. The review scrutinizes these studies, shedding light on their methodologies, including the use of recent machine learning and pattern recognition approaches coupled with different supervision strategies. However, comparing the results from different papers is challenging due to variations in the datasets used.Discussion: Considering these findings, this review underscores the urgent need for more robust and reliable datasets in the field of OSCC detection. Furthermore, it highlights the potential of advanced techniques such as multi‐task learning, attention mechanisms, and ensemble learning as crucial tools in enhancing the accuracy and sensitivity of OSCC detection through oral photographs.Conclusion: These insights collectively emphasize the transformative impact of AI‐driven approaches on early OSCC diagnosis, with the potential to significantly improve patient outcomes and healthcare practices.

Introduction: Oral Squamous Cell Carcinomas (OSCC) are mostly related to tobacco consumption eventually associated to alcohol (Smoker/Drinker patients: SD), but 25-30% of the patients have no identified risk factors (Non-Smoker/Non-Drinker patients: NSND). We hypothesized that these patients have distinguishable immune profiles that could be useful for prognosis.Materials and Methods: Cells present in immune tumor microenvironment (TME) and blood from 87 OSCC HPV-negative patients were analyzed using a multiparameter flow cytometry assay, in a prospective case-control study. Cytokine levels in tumor supernatants and blood were determined by a cytometric bead array (CBA) assay.Results: Normal gingiva and blood from healthy donors (HD) were used as controls. A significant increase of granulocytes (p<0.05 for blood), of monocytes-macrophages (p<0.01 for blood) and of CD4⁺ T cells expressing CD45RO and CCR6 (p<0.001 for blood; p<0.0001 for TME) as well as higher levels of IL-6 (p<0.01 for sera, p<0.05 for tumor supernatant) were observed in SD patients as compared to NSND OSCC patients and HD. High percentages of CD4⁺ T cells expressing CD45RO and CCR6 cells in tumor tissue (p=0.05) and blood (p=0.05) of SD OSCC patients were also associated with a poorer prognosis while a high percentage of regulatory T cells (Treg) in tumor tissue was associated with a more favorable prognostic factor (p=0.05). Also, a higher percentage of blood CD8⁺ T lymphocytes among CD45⁺ cells in NSND patients was associated with a better disease-free survival (p=0.004).Conclusion: Granulocytes, monocytes-macrophages, and CD4⁺ T cells expressing CD45RO and CCR6 in blood and TME as well as serum IL-6 can therefore distinguish OSCC SD and NSND patients. Quantifying the proportion of CD4⁺ T cells expressing CD45RO and CCR6 and of Treg in SD patients and CD8⁺ T cells in NSND patients could help defining the prognostic of OSCC patients.

Introduction:One of the dental surgeon's roles is to identify, diagnose and supervise the treatment of oral pathologies (OP), and/or to refer patients to the appropriate specialists. Today, the delay in diagnosing patients with OP is very long and may impact their prognosis.Objective:We conducted a national survey of French practitioners to estimate their knowledge and practices regarding the management of these pathologies.Methods:About 15,000 dentists were contacted by email to complete a questionnaire designed to assess the diagnoses and management of OP.Results:Overall, for 560 answers (3.8%), DS did not declare any significant difficulties in diagnosing and managing any kind of pathologies. However, we were able to distinguish statistical differences among practitioners depending on the type of lesions and between the practitioners themselves. The associated factors were gender, graduation year, mode of practice and continuing education over the last 5 years. The majority of DS justified their diagnostic difficulties by the fact that they do not encounter patients with these types of pathology frequently enough, and by insufficient education.Conclusion:According to our study, it is necessary to reinforce the training and the continuing medical education of DS regarding OP, especially through the use of practical cases.

Key Clinical Message: Cytomegalovirus (CMV) ulcerations are rare clinical entities, but their occurrence is favored in immunocompromised patients who present a favorable environment for opportunistic infections. We describe the case of a patient treated for a systemic lupus erythematosus suffering from deep oral ulcerations. The case illustrates the complexity of establishing a precise etiological diagnosis of CMV lesions, as the diagnostic hypothesis can be varied: related to an immunodeficiency disorder or drug‐induced toxidermia.

Early detection of oral cavity cancers is critical for improving patient survival rates and treatment efficacy. In this context, this study evaluates the potential of computer vision models as diagnostic tools for identifying cancerous lesions from oral cavity photographs. A comparative study was conducted using modern deep learning-based object detection models to detect lesions, such as squamous cell carcinoma, from a biopsy-proven dataset of oral lesion photography. A comprehensive workflow was developed to evaluate and compare these models, including fine-tuning, hyperparameters optimization, and performances assessment using precision, sensitivity, and specificity metrics. Additionally, the various parameters influencing the model were systematically measured and analyzed, distinguishing it from previous studies and providing a novel and comprehensive assessment of detection methodologies in the field. The studied models demonstrated high performance in a single-class detection setting, effectively localizing oral lesions with promising precision. However, in a two-class detection setting, distinguishing between malignant and benign lesions proved challenging, indicating a need for further refinement. This study underscores the potential of artificial intelligence in aiding early detection of oral cancers while identifying areas for improvement (notably: small lesion detection and distinguishing between malignant and benign). These findings provide a foundation for advancing medical computer vision tools to support early diagnosis and improve patient outcomes.