OBJECTIVE: this study aims to compare the clinical outcomes of traditional and digital crown extension guides in the aesthetic restoration of anterior teeth. Additionally, the study will analyze the differences in the results of various digital crown extension guides in anterior aesthetic restorations.
METHODS: Sixty-two patients who required aesthetic restoration of their anterior teeth were selected for this study. The patients had a total of 230 anterior teeth and were randomly divided into three groups: a control group of 22 cases who received diagnostic wax-up with pressure film, an experimental group 1 of 20 cases who received 3D printed digital models with pressure film, and an experimental group 2 of 20 patients who received digital dual-positioning guides. The control group had a total of 84 anterior teeth, experimental group 1 had 72 anterior teeth, and experimental group 2 had 74 anterior teeth. The study compared three methods for fabricating crown extension guides: the control group used the diagnostic wax-up plus compression film method, while experimental group 1 used compression film on 3D printed models and experimental group 2 used 3D printed digital dual-positioning crown extension guides. After the crown lengthening surgery, the control group patients wore DMG resin temporary crown material for gingival contouring, while the experimental group patients wore 3D printed resin temporary crowns for the same purpose. The patients were followed up in the outpatient clinic after wearing temporary crowns for 1 month, 3 months, and 6 months, respectively. The clinical results were evaluated in terms of marginal fit, red aesthetic index, and white aesthetic index.
RESULTS: Based on the statistical analysis, the experimental group required significantly fewer follow-up visits and less time for guide design and fabrication compared to the control group. Additionally, the surgical time for the experimental group was significantly shorter than that of the control group. During the postoperative period between the 1st and 3rd month, the PES index scores for the marginal gingival level, proximal, and distal mesiodistal gingival papillae of the experimental group showed a trend of superiority over those of the control group. By the 6th month, the marginal gingival level exhibited a significant difference between the experimental and control groups. The experimental group demonstrated superior results to the control group in terms of shape, contour, and volume of the teeth, color, surface texture, and transparency of the restorations, and features during the 1st and 3rd postoperative months. In the 6th month, the comparative results indicated that the experimental group continued to exhibit superior outcomes to the control group in terms of the shape, color, surface texture, and transparency of the restorations, as well as the characteristics of the teeth. Additionally, the experimental group demonstrated significantly fewer gingival alterations than the control group at 1 month, 3 months, and 6 months post-procedure, with this difference being statistically significant. Furthermore, the combination of 3D printing technology and restorative techniques was utilized, resulting in consistent patient satisfaction.
CONCLUSIONS: Digitalisation plays an important role in anterior aesthetic restorations. The use of digital technology to manage the entire process of anterior cosmetic restorations can improve restorative results, reduce the number of follow-up appointments, shorten consultation time, and achieve better patient satisfaction.

This study aimed to develop and validate evaluation metric for an automated smile classification model termed the \"smile index.\" This innovative model uses computational methods to numerically classify and analyze conventional smile types.
The datasets used in this study consisted of 300 images to verify, 150 images to validate, and 9 images to test the evaluation metric. Images were annotated using Labelme. Computational techniques were used to calculate smile index values for the study datasets, and the resulting values were evaluated in three stages.
The smile index successfully classified smile types using cutoff values of 0.0285 and 0.193. High accuracy (0.933) was achieved, along with an F1 score greater than 0.09. The smile index successfully reclassified smiles into six types (low, low-to-medium, medium, medium-to-high, high, and extremely high smiles), thereby providing a clear distinction among different smile characteristics.
The smile index is a novel dimensionless parameter for classifying smile types. The index acts as a robust evaluation tool for artificial intelligence models that automatically classify smile types, thereby providing a scientific basis for largely subjective aesthetic elements.
The computational approach employed by the smile index enables quantitative numerical classification of smile types. This fosters the application of computerized methods in quantifying and analyzing real smile characteristics observed in clinical practice, paving the way for a more objective evidence-based approach to aesthetic dentistry.