Enamel Caries Detection and Diagnosis: An Analysis of Systematic Reviews

Detection and diagnosis of caries—typically undertaken through a visual-tactile examination, often with supporting radiographic investigations—is commonly regarded as being broadly effective at detecting caries that has progressed into dentine and reached a threshold where restoration is necessary. With earlier detection comes an opportunity to stabilize disease or even remineralize the tooth surface, maximizing retention of tooth tissue and preventing a lifelong cycle of restoration. We undertook a formal comparative analysis of the diagnostic accuracy of different technologies to detect and inform the diagnosis of early caries using published Cochrane systematic reviews. Forming the basis of our comparative analysis were 5 Cochrane diagnostic test accuracy systematic reviews evaluating fluorescence, visual or visual-tactile classification systems, imaging, transillumination and optical coherence tomography, and electrical conductance or impedance technologies. Acceptable reference standards included histology, operative exploration, or enhanced visual assessment (with or without tooth separation) as appropriate. We conducted 2 analyses based on study design: a fully within-study, within-person analysis and a network meta-analysis based on direct and indirect comparisons. Nineteen studies provided data for the fully within-person analysis and 64 studies for the network meta-analysis. Of the 5 technologies evaluated, the greatest pairwise differences were observed in summary sensitivity points for imaging and all other technologies, but summary specificity points were broadly similar. For both analyses, the wide 95% prediction intervals indicated the uncertainty of future diagnostic accuracy across all technologies. The certainty of evidence was low, downgraded for study limitations, inconsistency, and indirectness. Summary estimates of diagnostic accuracy for most technologies indicate that the degree of certitude with which a decision is made regarding the presence or absence of disease may be enhanced with the use of such devices. However, given the broad prediction intervals, it is challenging to predict their accuracy in any future “real world” context.