Using autofluorescence to detect bacterial contamination in root fractures.

OBJECTIVES: Conventional methods for detecting root fractures cannot assess their depth or bacterial contamination. This study was designed to measure the autofluorescence emitted from a root fracture, with the aim of determining whether this is a suitable method for quantifying the depth and bacterial invasion of a fracture. METHODS: This in vitro study investigated 33 mandibular second molars with periapical lesions that had been extracted after finding root fractures in endodontically treated teeth during intentional replantation or diagnostic surgery. The root fractures were scanned using a fluorescence technique, and the association between fluorescence parameters and fracture depth was analyzed. The significance of the association between the red fluorescence among autofluorescence parameters and bacterial contamination within the fracture was examined. RESULTS: When the depth of the root fractures was evaluated by micro computed tomography, the scattering of light in the fractures increased with depth, and there was a gradual increase in the quantitative fluorescence parameter indicating the deepest point (ΔFmax) in the fractures. In addition, we observed red fluorescence on the outer surface of deeper fractures. The tooth fractures exhibiting red fluorescence were evaluated for bacterial contamination associated with red-fluorescent porphyrin, which revealed bacterial invasion into these fractures. On the other hand, non-red-fluorescing fractures contained necrotic tissue, debris, and irritants. CONCLUSIONS: This viable fluorescent technique can potentially quantify the depth of root fractures and be used as a risk indicator for root fractures with periodontal inflammation. CLINICAL SIGNIFICANCE: The auto-fluorescence technique can be used to detect depth and bacterial contamination of root fractures. It is postulated that the auto-fluorescence can be used as a risk indicator of deep fractures and can replace conventional fracture detection methods.