PubMed · 13968088
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B G BIBBY, H M AVERILL. Caries control through phosphates.. https://doi.org/10.1177/00220345630420015501
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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.
The objective of this study was to evaluate the effect of in vitro degradation and mechanical loading on microtensile bond strength (MTBS) and microleakage (ML) of a resin composite to dentin using a self-etching primer adhesive [Clearfil SE Bond (SEB)] under two hydration statuses. Twenty-four flat dentin surfaces were divided in groups: 1) blot-dried, 2) air-dried. SEB was applied and resin buildups were performed with Tetric Ceram. Specimens were divided in four subgroups: a) sectioned into beams, b) load cycled, c) beams were immersed in NaOCl for 5 h, d) load cycled and immersed in NaOCl. Beams were tested in tension. For ML testing, 80 Class V cavities were prepared and molars divided in subgroups as described above (in group c and d, specimens were kept in distilled water for 1 year, instead of the 5-h NaOCl immersion). ML was assessed by dye penetration. Analysis of variance and multiple comparisons tests were used for MTBS. For ML, Mann-Whitney U and Wilcoxon matched pairs signed ranked were used (p<0.05). SEB applied to completely dehydrated dentin produced the highest MTBS, at 24-h evaluation. In vitro degradation always decreased MTBS, and fatigue loading only diminished MTBS on dehydrated dentin. Load cycling increased dye penetration on dentin margins. Degradation always increased ML in both enamel and dentin margins.
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