Targeted enamel remineralization with mineral-loaded starch particles.

Background: Noninvasive caries treatments work topically, which may limit efficacy. The authors hypothesized that an alternative approach using mineral-loaded particles designed to target the subsurface of noncavitated caries lesions could be advantageous. This study shows in vitro proof-of-concept.

Convolution Neural Networks and Targeted Fluorescent Nanoparticles to Detect and ICDAS Score Caries.

Previous work has shown targeted fluorescent starch nanoparticles (TFSNs) can label the subsurface of carious lesions and assist dental professionals in the diagnostic process. In this study, we aimed to evaluate the potential of using artificial intelligence (AI) to detect and score carious lesions using ICDAS in combination with fluorescent imaging following application of TFSNs on teeth with a range of lesion severities, using ICDAS-labeled images as the reference standard. A total of 130 extracted human teeth with ICDAS scores from 0 to 6 were selected by a calibrated cariologist.

Early occlusal caries detection using targeted fluorescent starch nanoparticles.

Objectives: We have previously shown fluorescent cationic starch nanoparticles (FCSNs) penetrate enamel surface porosity of active carious lesions, potentially aiding their detection. Here, we evaluate the in vitro diagnostic accuracy of FCSNs in detecting occlusal caries compared to histologic reference standard.

Methods: 100 extracted human teeth were selected with sound (50), or either non-cavitated (25) or cavitated (25) lesions.