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Aim: Accurately identifying primary lesions in oral medicine, particularly elementary white lesions, is a significant challenge, especially for trainee dentists. This study aimed to develop and evaluate a deep learning (DL) model for the detection and classification of elementary white mucosal lesions (EWMLs) using clinical images. Materials and Methods: A dataset was created by collecting photographs of various oral lesions, including oral leukoplakia, OLP plaque-like and reticular forms, OLL, oral candidiasis, and hyperkeratotic lesions from the Unit of Oral Medicine. The SentiSight.AI (Neurotechnology Co.®, Vilnius, Lithuania) AI platform was used for image labeling and model training. The dataset comprised 221 photos, divided into training (n = 179) and validation (n = 42) sets. Results: The model achieved an overall precision of 77.2%, sensitivity of 76.0%, F1 score of 74.4%, and mAP of 82.3%. Specific classes, such as condyloma and papilloma, demonstrated high performance, while others like leucoplakia showed room for improvement. Conclusions: The DL model showed promising results in detecting and classifying EWMLs, with significant potential for educational tools and clinical applications. Expanding the dataset and incorporating diverse image sources are essential for improving model accuracy and generalizability. [ABSTRACT FROM AUTHOR]

Introduction: Exodontia is commonly considered as a risk factor for the development of medication‐related osteonecrosis of the jaw (MRONJ) in individuals exposed to bone modifying agents. This study was aimed at assessing the efficiency and safety of a gaseous oxygen–ozone mixture as an adjuvant to a standard exodontia to reduce the risk of MRONJ development. Methods: A randomized, open‐label, phase II, single‐center clinical trial involving 117 patients at risk of MRONJ was conducted. The study protocol tested injections of an oxygen–ozone mixture in the post‐extraction site. Participants were randomly assigned to two groups: oxygen‐ozone therapy, and standard tooth extraction protocol. Post‐extraction wound healing was assessed using the Inflammatory Proliferative Remodeling (IPR) Wound Healing Scale. Results: The oxygen‐ozone therapy group exhibited a significant improvement in wound healing post‐extraction during the inflammatory and proliferative phases, as indicated by the IPR scale scores at 3–5 days (p = 0.006) and 14 days (p < 0.001) respectively. Conclusion: Oxygen–ozone therapy shows promise in improving post‐extraction healing in patients at risk of MRONJ. Future studies with larger sample sizes and multicenter collaborations are recommended to confirm the validity of these findings and explore the long‐term efficacy of ozone therapy. [ABSTRACT FROM AUTHOR]

ABSTRACT Objective Materials and Methods Results Conclusions Trial Registration Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In recent years, deep learning (DL) has emerged as a powerful tool for the automated detection and classification of oral lesions. This research, by conducting a scoping review and meta‐analysis, aims to provide an overview of the progress and achievements in the field of automated detection of oral lesions using DL.A scoping review was conducted to identify relevant studies published in the last 5 years (2018–2023). A comprehensive search was conducted using several electronic databases, including PubMed, Web of Science, and Scopus.Two reviewers independently assessed the studies for eligibility and extracted data using a standardized form, and a meta‐analysis was conducted to synthesize the findings.Fourteen studies utilizing various DL algorithms were identified and included for the detection and classification of oral lesions from clinical images. Among these, three were included in the meta‐analysis. The estimated pooled sensitivity and specificity were 0.86 (95% confidence interval [CI] = 0.80–0.91) and 0.67 (95% CI = 0.58–0.75), respectively.The results of meta‐analysis indicate that DL algorithms improve the diagnosis of oral lesions. Future research should develop validated algorithms for automated diagnosis.Open Science Framework (https://osf.io/4n8sm) [ABSTRACT FROM AUTHOR]

The use of digital health technologies, including telemedicine and teledentistry, has become a necessity in healthcare due to the SARS-CoV-19 pandemic. These technologies allow for the reduction of the workload of healthcare providers and the improvement of patient outcomes in cases of remote monitoring, diagnosis, and communication. While there are no doubtful benefits, there are some counterparts, such as concerns about clinical risks, data security, and privacy protection. This paper aims to review the regulations regarding the use of digital health apps and software in healthcare. This scoping review followed the PRISMA-ScR guidelines and the five-step framework of Arksey and O'Malley. Study selection was based on eligibility criteria that were defined using the population-exposure framework. The review of the articles selected (n = 24) found that the majority focused on data security policies in the healthcare industry, highlighting the need for comprehensive regulations and app control systems to protect patient data. The articles also emphasized the requirement for more appropriate research and policy initiatives to improve data security practices and better address privacy and safety challenges related to health-related apps. The review recognized that papers did not report consistent standards in professional obligation and informed consent in online medical consultations, with potential risks for data privacy, medical liabilities, and ethical issues. Digital health has already revolutionized medical service delivery through technology but faces some challenges, including the lack of standardized protocols for handling sensitive patient data and the absence of common legislative provisions, raising concerns about confidentiality and security. To address these issues and deficiencies, regulatory compliance is crucial to clarify and harmonize regulations and provide guidelines for doctors and the health system. In conclusion, regulating patient data, clarifying provisions, and addressing informed patients are critical and urgent steps in maximizing usage and successful implementation of telemedicine. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to perform a descriptive analysis of oropharyngeal adverse events (AEs) related to drugs and/or vaccines in order to provide useful information for clinicians., Methods: Data related to three regions of Italy were analyzed from 2019 to 2021 by using the National Pharmacovigilance database., Results: Among overall 67,384 cases, 2773 (4.1%) reported at least one oropharyngeal AE. Most cases referred to females (71.0%) and adults (70.8%). The majority of cases were reported as not serious (68.4%) and the outcome was mainly positive (73.5%). The cases related to drugs (52.2%) were slightly more than those related to vaccines (47.8%), the latter nearly completely represented by COVID-19 vaccines. Among 3324 oropharyngeal AEs, the most commonly reported were oropharyngeal conditions (65.9%). The most reported AEs related to vaccines were paresthesia oral and oropharyngeal pain, whereas the most reported AEs related to drugs were throat tightness and angioedema. A marked under-reporting of osteonecrosis of the jaw (2.9%) was observed, despite this risk was well documented in the same country., Conclusions: This analysis suggested an under-reporting of oropharyngeal AEs and the need to better train dentists, dental hygienists, and also general practitioners., (© 2024 The Author(s). Oral Diseases published by John Wiley & Sons Ltd.)