Dastoori, MahmoudAthanasiou, Athanasios E2021-11-182021-11-182018304-2018.39https://repository.mbru.ac.ae/handle/1/603Background: To assess the accuracy of digital models generated using commercially available software to predict anterior teeth root inclination characteristics and compare the results to relevant data obtained from CBCT images. Material and Methods: Following sample size calculation and after application of inclusion and exclusion criteria, pre-treatment maxillary and mandibular plaster models and the corresponding CBCT scans of 31 patients attending a private orthodontic clinic were selected. The subjects were 10 males and 21 females with age range 12 to 40 years. Plaster models were scanned using the high resolution mode of an Ortho Insight 3D scanner and CBCT scans were taken using a Kodak 9500 Cone Beam 3D System machine. The teeth on the digital scans were segmented and virtual roots were predicted and constructed by the Ortho Insight 3D software. The long axes of the predicted roots and the actual roots, as segmented from the CBCT images, were computed using best-fit lines. The inter-axis angle was used to assess error in root inclination prediction by the software. Mann-Whitney and Kruskal-Wallis tests were used. Intra-examiner error was evaluated using the Bland-Altman method. Results: The maximum disparity in angle between images derived from digital models and CBCT data was almost 40 degrees (upper left canine). The upper and lower canines produced the worst results, followed by the lower lateral incisors. The upper central incisors showed the best results, although the maximum angle of difference exceeded 20 degrees (with the median around 8 degrees). Conclusions: Root morphology imaging prediction is not a primary function of this software and this study confirmed its limitation as a sole tool in routine clinical applications. At present these predictions cannot be considered accurate or reliable unless correlated clinically with a radiographic image.enDigital modelsCBCTTooth root inclination prediction softwareArticle