PubMed · 289985
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I L Shannon, J N Trodahl. 1978. A clinically effective chemical preventive dentistry program.. https://pubmed.ncbi.nlm.nih.gov/289985/
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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.
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Current dental diagnostic methods can detect caries but cannot quantify the mineral status of a lesion. Quantitative light-induced fluorescence (QLF) measures the percentage of fluorescence change of demineralized enamel with respect to surrounding sound enamel, and relates it directly to the amount of mineral lost during demineralization. Development of caries-like lesions and subsequent remineralization of the lesions were monitored by QLF. The results showed that the percentage of fluorescence change (Delta Q) increased linearly with the demineralization time and decreased with increased remineralization time. Stained teeth were whitened with a bleaching agent and the change in stain intensity (Delta E) was quantified using QLF. The results showed that Delta E decreased linearly as the tooth regained its natural color. Factors that might affect the use of QLF to detect and quantify caries were also examined. It was concluded that QLF could be used to detect and longitudinally monitor the progression or remineralization of incipient caries, however lesion detection may be limited by the presence of saliva or plaque and enhanced by staining. The change in shade of discolored teeth by whitening agents could be quantitatively measured by QLF.