PubMed · 14863780
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1951. DIET and tooth development.. https://doi.org/10.1111/j.1753-4887.1951.tb02649.x
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Artificial intelligence (AI) has been proposed as a tool to improve dental caries detection across imaging modalities; however, its clinical value remains uncertain. This umbrella review aimed to synthesize and critically appraise systematic reviews evaluating AI for caries detection and diagnosis. An umbrella review was conducted following PRIOR guidance (PROSPERO CRD420261340728). Searches were performed in MEDLINE, Embase, Scopus, Web of Science, and Google Scholar up to 15 March 2026. Methodological quality was assessed using AMSTAR 2, and overlap of primary studies was quantified using the corrected covered area (CCA). Seventeen systematic reviews were included, of which five reported diagnostic test accuracy meta-analyses using bivariate or HSROC models. Across these meta-analyses, pooled sensitivity ranged from 0.76 to 0.94 and specificity from 0.85 to 0.91. Most systems were based on deep learning models applied to bitewing radiographs and intraoral photographs. However, substantial heterogeneity was observed in imaging modalities, lesion thresholds, analytical tasks, and evaluation metrics. In addition, a high degree of overlap across reviews and recurrent methodological limitations, including reliance on retrospective datasets, limited external validation, and inconsistent reporting, substantially weaken the reliability of the evidence. Although AI models demonstrate high diagnostic performance under experimental conditions, current evidence does not support their use as stand-alone diagnostic tools. Their clinical applicability remains limited, and implementation should be restricted to decision-support contexts until robust prospective validation demonstrates meaningful impact on clinical decision-making and patient outcomes.
Caries-affected dentin contains less mineral and more water than surrounding normal dentin. Such dentin should be less stiff and should shrink more than normal dentin when dried. The purpose of this study was to test the relationships between the stiffness, shrinkage, and water content of caries-affected versus normal dentin. Extracted human carious third molars were stained with caries-detector dye and the occlusal surfaces ground down until only caries-affected dentin remained, surrounded by normal dentin. Dentin disks were prepared from these crowns placed in a aluminum well positioned under a modified thermal mechanical analyzer. Changes in specimen height and stiffness were measured following drying or static loading in both caries-affected and surrounding normal dentin. Core samples of these two types of dentin were used to gravimetrically measure water content. Two-way ANOVA and regression analysis was used to test the relationship between shrinkage versus water content, stiffness versus water content, and stiffness versus shrinkage. Caries-affected dentin was found to be less stiff and contained more water than normal dentin (p < 0.05). Regression analysis revealed highly significant inverse relationships between stiffness and water content, and stiffness and shrinkage (p < 0.0005).
The different types of hard- and soft tissues that are involved in the formation of the oral cavity are subject of numerous diseases. Albeit dental caries and periodontitis comprise the most common oral disease entities, also several premalignant and malignant conditions can occur within the oral cavity. There exists are wide armamentarium for the prevention of both, dental caries and periodontitis. Preventive strategies are even more important regarding oral malignancies since specifically these entities commonly provide only poor prognosis if not treated in early stages. The present contribution aims to present the pathogenic background of the most common oral diseases and the most effective methods for their prevention.