PubMed · 8409018
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H Pinkney. 1993. Diagnosing caries.. https://doi.org/10.14219/jada.archive.1993.0187
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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.
Biomechanical properties of bonded dentin are important factors for resin restoration. We evaluated the hardness and elastic modulus of bonded sound and caries-affected primary tooth dentin using a one-step adhesive system, and observed the microstructure of the bonded interface. Six sound and six carious primary teeth were used. For sound teeth, flat occlusal dentin surfaces were prepared with a water-cooled high-speed diamond bur. For carious teeth, infected dentin was stained with a caries detector and removed with a water-cooled low-speed round steel bur and hand instruments. The prepared dentin was bonded with One-Up Bond F Plus (Tokuyama Dental Co., Tokyo, Japan). The resin-dentin interface and dentin beneath the interface were measured with a nano-indentation tester and observed with SEM and TEM. For both the carious and sound teeth, there was no significant difference between the hardness of the interfacial dentin and dentin 10-80 microm beneath the interface. However, the Young's modulus of the interfacial dentin was significantly lower than the dentin 40-80 microm (carious teeth) or 50-80 microm (sound teeth) beneath the interface. Both the hardness and Young's modulus of the interfacial dentin were not significantly different between the carious and sound teeth. Compared to the sound dentin, the hybrid layer on the caries-affected dentin was thicker and exhibited more complicated morphologic features. The thickness of the hybrid layers was generally less than 1 microm.