Aim: The aim of this study was to evaluate the segmentation accuracy of dentition testing four free-source semi-automatic software. Materials and methods: A total of 20 cone-beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of maxillary and mandibular dentition. The software tested were Invesalius, ITK-Snap, 3D Slicer and Seg3D. Each tooth model was also manually segmented (Mimics software) and set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the teeth models obtained with semi-automatic software and the GS model, and to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the tooth models compared to the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analysed to perform software comparisons. Result: Statistically significant differences were found in the volumetric and matching percentage data (p < 0,05). Invesalius was the most accurate software for 3D rendering of the dentition with a volumetric bias (Mimics) ranging from 4,59 mm3 to 85,79 mm3; instead, ITK-SNAP showed the higher volumetric bias, ranging from 30,22 mm3 to 319,83 mm3. The dis-matched area was mainly located at the radicular region of the teeth. Volumetric data showed excellent inter-software reliability with coefficient values ranging from 0,951 to 0,997. Conclusio: Different semi-automatic software algorithms could generate different patterns of inaccuracy error in the segmentation of teeth. Schlagwörter: 3D rendering, CBCT, cone-beam computed tomography, digital dentistry, digital orthodontics, oral surgery, orthodontics, segmentation

Purpose: To propose a method to measure the esthetics of the smile and to report its validation by means of an intra-rater and inter-rater agreement analysis. Materials and methods: Ten variables were chosen as determinants for the esthetics of a smile: smile line and facial midline, tooth alignment, tooth deformity, tooth dischromy, gingival dischromy, gingival recession, gingival excess, gingival scars and diastema/missing papillae. One examiner consecutively selected seventy smile pictures, which were in the frontal view. Ten examiners, with different levels of clinical experience and specialties, applied the proposed assessment method twice on the selected pictures, independently and blindly. Intraclass correlation coefficient (ICC) and Fleiss' kappa) statistics were performed to analyse the intra-rater and inter-rater agreement. Results: Considering the cumulative assessment of the Smile Esthetic Index (SEI), the ICC value for the inter-rater agreement of the 10 examiners was 0.62 (95% CI: 0.51 to 0.72), representing a substantial agreement. Intra-rater agreement ranged from 0.86 to 0.99. Inter-rater agreement (Fleiss' kappa statistics) calculated for each variable ranged from 0.17 to 0.75. Conclusion: The SEI was a reproducible method, to assess the esthetic component of the smile, useful for the diagnostic phase and for setting appropriate treatment plans. Schlagwörter: classification, diagnosis, esthetics, index, smile