BMC Oral Health (Feb 2024)
Do metal artifact reduction algorithms influence the detection of implant-related injuries to the inferior alveolar canal in CBCT images?
Abstract
Abstract Background The routine application of dental implants for replacing missing teeth has revolutionized restorative and prosthetic dentistry. However, cone beam computed tomography (CBCT) evaluations of structures adjacent to the implants are limited by metal artifacts. There are several methods for reducing metal artifacts, but this remains a challenging task. This study aimed to examine the effectiveness of metal artifact reduction (MAR) algorithms in identifying injuries of implants to the inferior alveolar canal in CBCT images. Method In this in vitro study, mono-cortical bone windows were created and the inferior alveolar canal was revealed. Using 36 implants, pilot drill and penetration damage of the implant tip into the canal was simulated and compared to the control implants with distance from the canal. CBCT images were evaluated by four experienced observers with and without the MAR algorithm and compared to direct vision as the gold standard. The values of accuracy, sensitivity, and specificity were obtained and compared by receiver operating characteristic (ROC) curve (α = 0.05). Result The area under the ROC curve values for detection of pilot drill injuries varied between 0.840–0.917 and 0.639–0.854 in the active and inactive MAR conditions, respectively. The increase in ROC area was only significant for one of the observers (P = 0.010). For diagnosing penetrative injuries, the area under the ROC curve values was between 0.990–1.000 and 0.722–1.000 in the active and inactive MAR conditions, respectively. The improvement of ROC curve values in active MAR mode was only significant for one of the observers (P = 0.006). Conclusion Activation of MAR improved the diagnostic values of CBCT images in detecting both types of implant-related injuries to the inferior alveolar canal. However, for most observers, this increase was not statistically significant.
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