ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
OBJECTIVES: This systematic review and meta-analysis aims to evaluate the evidence on the malignant potential of oral lichenoid conditions (OLCs) including oral lichen planus (OLP), oral lichenoid lesions (OLL), and lichenoid mucositis dysplasia (LMD). In addition, it aims to compare the rate of malignant transformation (MT) in OLP patients diagnosed according to different diagnostic criteria, and to investigate the possible risk factors for OLP MT into OSCC. MATERIALS AND METHODS: A standardized search strategy was applied across four databases (PubMed, Embase, Web of Science, and Scopus). Screening, identification and reporting followed the PRISMA framework. Data on MT were calculated as a pooled proportion (PP), subgroup analyses and possible risk factors for MT were pooled as odds ratios (ORs). RESULTS: Among 54 studies with 24,277 patients, the PP for OLCs MT was 1.07% (95% CI [0.82, 1.32]). The estimated MT rate for OLP, OLL and LMD was 0.94%, 1.95% and 6.31%, respectively. The PP OLP MT rate using the 2003 modified WHO criteria group was lower than that using the non-2003 criteria (0.86%; 95% CI [0.51, 1.22] versus 1.01%; 95% CI [0.67, 1.35]). A higher odds ratio of MT was observed for red OLP lesions (OR = 3.52; 95% CI [2.20, 5.64]), smokers (OR = 1.79; 95% CI [1.02, 3.03]), alcohol consumers (OR = 3.27, 95% CI [1.11, 9.64]) and those infected with HCV (OR = 2.55, 95% CI [1.58, 4.13]), compared to those without these risk factors. CONCLUSIONS: OLP and OLL carry a low risk of developing OSCC. MT rates differed based on diagnostic criteria. A higher odds ratio of MT was observed among red OLP lesions, smokers, alcohol consumers, and HCV-positive patients. These findings have implications for practice and policies.
