Aim: In recent years, biomarkers in saliva have been of great interest as promising non-invasive tools for the diagnosis and prognostic evaluation of oral potentially malignant disorders (OPMDs). The existing literature includes a substantial number of systematic reviews and meta-analyses investigating the role of salivary biomarkers in OPMDs; however, the reliability and usefulness of their findings depend to a large extent on the methodological quality of the reviews. The purpose of this article is therefore to assess the existing evidence on the potential usefulness of salivary biomarkers in OPMDs and to evaluate the methodological rigor and quality of available systematic reviews and meta-analyses, as well as the limitations and gaps in knowledge. Methods: The literature was searched for systematic reviews and meta-analyses that assessed the potential of salivary biomarkers in OPMDs in PubMed, Google Scholar, Scopus, and ScienceDirect. AMSTAR-2 was used to assess the quality of the systematic reviews and meta-analyses included. Results: Eighteen articles meeting the predefined eligibility criteria were included, of which 11 carried out meta-analyses, while the remaining 7 were only systematic reviews. The corrected covered area (CCA) of 7.39% indicated moderate overlap between studies. The main markers studied were lactate dehydrogenase (LDH), microRNAs (miRNAs), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). All included systematic reviews and meta-analyses were found to have critically low confidence in quality. Conclusion: Strong adherence to established quality assessment standards is recommended to improve the reliability and validity of the evidence for all forthcoming systematic review compilations.
Background: Oral submucous fibrosis (OSMF) is a long-standing, disabling, potentially malignant oral condition mostly observed across South Asian populations. Existing treatment modalities mainly provide symptomatic relief with limited influence on disease progression. As an autologous biological preparation rich in growth factors, platelet-rich plasma (PRP) has recently introduced a novel regenerative approach aimed at optimizing treatment efficacy for patients with OSMF. Objective: To systematically assess the effectiveness of PRP in the management of OSMF, with particular focus on improvements in mouth opening and burning sensation compared with conventional therapeutic approaches. Materials and Methods: A comprehensive literature search up to June 2025 was conducted on PubMed, Cochrane Library, Turning Research into practice, and gray literature sources following the PRISMA framework (PROSPERO ID: CRD420251082062). We selected comparative clinical investigations and randomized controlled trials evaluating PRP treatment for OSMF. Quantitative data were pooled using a random-effects model to determine mean differences with 95% confidence intervals, while heterogeneity across studies was appraised with the l 2 test. Results: Eight eligible studies, comprising five randomized controlled trials and three comparative non-randomized studies, were included in the review. PRP was evaluated against conventional interventions including triamcinolone, hyaluronidase, and curcumin. Most studies reported improvements in mouth opening following PRP therapy, whereas findings related to burning sensation and pain were inconsistent. Meta-analysis of two randomized controlled trials ( n = 154) demonstrated a significant improvement in mouth opening with PRP, while no statistically significant benefit was observed for burning sensation or pain compared with conventional therapies. Conclusion: Available evidence suggests that PRP has the potential to improve mouth opening when used as an adjunctive therapy in patients with OSMF. However, its effectiveness in reducing burning sensation and pain remains uncertain due to limited and heterogeneous evidence. Future adequately powered randomized controlled trails trials using standardized treatment protocols and longer follow-up are needed to better define the role of PRP in OSMF management.
Objective: To evaluate the clinical and radiographic outcomes of guided immediate bicortical implant placement combined with a one-abutment-one-time (OAOT) protocol using prefabricated microgrooved abutments with zirconia crowns (Zircopings) and a fully digital prosthetic workflow in the maxillary aesthetic zone. Methods: In this non-randomized, single-arm prospective cohort study, 40 patients underwent flapless tooth extraction and guided placement of long implants engaging the nasal/palatal cortex (insertion torque ≥50 Ncm). Prefabricated custom Zircopings and composite provisional restorations were delivered immediately, followed by definitive lithium-disilicate crowns after 3 months, which served as the baseline for hard- and soft-tissue evaluation. Patients were assessed at 3, 6, and 12 months for implant and prosthesis survival, marginal bone level (MBL) changes, mid-facial mucosal recession, and interproximal papilla fill. Longitudinal changes were analyzed using the Friedman and Bonferroni-adjusted Wilcoxon signed-rank tests. Results: All 40 participants completed the 12-month follow-up. Implant and prosthesis survival were 100% (95% CI: 91.2%–100%). From the 3-month baseline to 12 months, mean MBL changes were 0.14 mm (95% CI: 0.01–0.27 mm; p = .0009) mesially and 0.09 mm (95% CI: −0.02 to 0.21 mm; p = .0009) distally. Mid-facial mucosal recession measured 0.06 ± 0.09 mm (95% CI: 0.03–0.09 mm; p = .0008). The mean Jemt papilla score improved from 2.88 ± 0.29 to 3.00 ± 0.00 ( p = .0012). Conclusion: Guided immediate bicortical implant placement with a digital OAOT protocol achieved excellent short-term clinical and radiographic outcomes in the maxillary aesthetic zone. Further controlled studies with longer follow-up are warranted.
Background and Objective: Artificial intelligence (AI) frameworks, particularly deep learning architectures, have emerged as powerful tools to optimize the objectivity and efficiency of dental age estimation. This umbrella review systematically synthesized and critically appraised the current evidence from existing systematic reviews regarding the diagnostic accuracy and clinical performance of AI-driven dental age estimation in forensic odontology. Methods: A comprehensive search protocol was executed across PubMed, PubMed Central, Google Scholar, and IEEE Xplore to identify systematic reviews published up to December 2024. The population of interest focused on systematic reviews evaluating age estimation accuracy in living or deceased individuals. Methodological quality and risk of bias of the included systematic reviews were independently evaluated using the AMSTAR 2 tool, and literature redundancy was assessed via the corrected covered area (CCA) index. Results: Out of 474 initially screened records, five systematic reviews tracking diverse machine learning networks were included. Convolutional neural networks (CNNs) and deep learning architectures consistently demonstrated superior diagnostic precision, yielding mean absolute errors frequently below 1.0 year and overall accuracies ranging from 94.7% to 99.98% compared to traditional manual approaches (e.g., Demirjian and Nolla methods). However, the AMSTAR 2 assessment revealed critical methodological vulnerabilities, grading all five included systematic reviews as having low quality due to systemic deficiencies in protocol registration, duplicate data extraction protocols, and risk of bias tracking. The calculated CCA demonstrated minimal primary study overlap across the synthesized literature. Conclusion: AI-driven dental age estimation models—specifically deep CNN frameworks—exhibit high precision and clinical reproducibility for forensic applications. However, current conclusions are limited by the low methodological quality of the underlying systematic reviews. Future research must prioritize standardized external validation across diverse ethnic populations and implement explainable AI models to ensure diagnostic transparency before integration into routine forensic casework.
Background: Oral squamous cell carcinoma (OSCC) remains a challenging issue in cancer treatment, owing to the lack of accurate diagnosis models, resulting in delayed detection and treatment. Histopathological assessment of tissue specimens is fundamental for the detection of OSCC. The occurrence of understated visual variations among early malignant and benign oral conditions leads to misclassification, affecting early identification and survival rates. Objectives: Therefore, this study develops a new Generative Adversarial Network-based Synthetic Image Generation with Deep Ensemble Pipeline (GANSIH-DEP) technique for enhanced OSCC detection and classification. The aim of the GANSIH-DEP technique is to balance the OSCC dataset via data augmentation and auxiliary classifier generative adversarial network (AC-GAN) based synthetic image generation, thereby improving the Deep Learning (DL) model performance on OSCC detection. Methods: Data augmentation takes place via geometric ways (rotation, flipping, scaling, translation) to raise data diversity during training with the same class labels. Moreover, the GANSIH-DEP technique utilizes AC-GAN to generate high-fidelity synthetic images to resolve the limited data availability and class imbalance problem, specifically enhancing the representation of rare and atypical OSCC classes, which are often underdiagnosed in small clinical datasets. Furthermore, the deep feature representations are extracted using a multi-branch convolutional neural network (CNN)-based model. To optimize the performance of a multi-branch Convolutional neural network (CNN) model, the Nadam optimizer is used for hyperparameter selection to accomplish stability and high convergence. For OSCC detection, the weighted deep learning ensemble (WDLE) model is used to enhance diagnostic performance by minimizing model bias and variance. Final, the Explainable Artificial Intelligence (XAI) using the Grad–Class Activation Maps (CAM) is integrated to provide visual explanations by highlighting the discriminative regions influencing the classification outcomes. Results: The experimental validation of the proposed model on the benchmark OSCC dataset exhibits an improved accuracy of 98.07%, demonstrating reduced false negatives, assuring that the early-stage lesions are not unnoticed. The enhanced performance significantly supports earlier clinical decision making, enhanced treatment planning, better prognosis, and enhanced long-term survival rate. Conclusion: The proposed model is found to be an efficient diagnostic tool for the robust OSCC detection process.
