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BACKGROUND: This study aimed to evaluate the incidence of implant failure in patients with oral lichen planus (OLP) and investigate the potential association between OLP and peri-implant diseases. MATERIALS AND METHODS: Embase, Web of Science, PubMed, and Scopus databases were searched for studies with no time restrictions. Meta-analysis was performed calculating pooled proportion of peri-implantitis (PI), peri-implant mucositis (PIM), and bleeding on probing (BOP) prevalence using fixed-effects model. Odds ratio and corresponding 95% CI were calculated to assess the potential risk of PI, PIM, and BOP in dental implant patients with OLP compared to healthy controls. RESULTS: Implant failure rate was 4.38% at the patient level and 4.37% at the implant level. Six patients (3.92%) from five studies were diagnosed with oral cancer after receiving implant. The prevalence of PI, PIM, and BOP at the implant level were 14.00%, 20.00%, and 40.00%, respectively. There was no significant difference in the occurrence of PI and PIM between OLP patients and healthy controls. CONCLUSIONS: Stabilized OLP is not considered a significant risk factor for peri-implant diseases. It is advised against placing implants or prostheses during the acute phase of the disease. Histopathological investigation to differentiate OLP from oral lichenoid dysplasia is crucial.
RESUMO
BACKGROUND: The objective of this study is to examine the application of AI algorithms in detecting OPMD and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes. MATERIALS AND METHODS: A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools. RESULTS: The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95%CI= [39.484, 118.623], I2 = 86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95%CI= [0.866,0.925]; I2 = 81%), specificity of 89.2% (95%CI= [0.851,0.922], I2 = 79%), and a high negative predictive value of 89.5% (95%CI= [0.851; 0.927], I2 = 96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95%CI [0.873,0.929], I2=4%), 87.0% (95%CI [0.813,0.912], I2=49%) and 90.1% (95%CI [0.860,0.931], I2=57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy. CONCLUSIONS: AI-based detection using clinical photography shows a high diagnostic odds ratio and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.
RESUMO
Introduction: The aim of this systematic review is to provide a clinical update of the current knowledge on COVID-19 and oral mucosal lesions, to analyze the types and prevalence of oral mucosal lesions in patients with COVID-19, and to clarify the potential association between COVID-19 and oral mucosal lesions. Methods: The literature search was conducted using PubMed, Web of Science, Scopus and the Cochrane Library, as well as literatures via manual searches of the reference lists of included studies. Studies published in English that mentioned oral mucosal lesions in patients with COVID-19 were included, resulting in a total of 31 studies. Results: Most of the included studies were considered to have a moderate to high risk of bias according to the Joanna Briggs Institute bias assessment tools. Based on COVID-19 severity, the characteristics and patterns of oral mucosal lesions in COVID-19 patients were described, analyzed and synthesized. Overall, ulcers without specific diagnosis had the highest prevalence in COVID-19 patients, followed by traumatic ulcers, candidiasis, petechiae and aphthous-like lesions. Homogeneity of data cannot be achieved in statical analysis, indicating randomness of outcome (ulcers without specific diagnosis, 95% CI: 28%-96%, I2 = 98.7%). Discussion: Given the limited evidence from currently available studies, the association between COVID-19 and oral mucosal lesions remains difficult to clarify. Healthcare professionals should be aware of the possible association between COVID-19 and oral mucosal lesions, and we hereby discuss our findings.
