The congenital absence of one or more teeth is a dental anomaly that frequently occurs in the world’s population with a wide variability of distribution. The aim of this study is to assess the current prevalence of dental agenesis in the permanent dentition (excluding third molars) using a sample of Italian orthodontic patients. Panoramic radiographs of 4006 Caucasian children between 9 and 16 years of age (1865 males and 2141 females) performed over a 5-year period (from 2010 to 2015) were carefully examined to identify congenital missing teeth. A chi-square test was used to determine the difference in the prevalence of hypodontia between genders and between arches. The prevalence of dental agenesis was 9% (9.1% for females and 8.7% for males). The most common congenitally missing teeth were the mandibular second premolars (20.3 and 18.1%) followed by the upper lateral incisors (17.8 and 17.7%) and the maxillary second premolars (7.4 and 6.3%). The absence of one tooth to five teeth was observed in 344 patients (8.6%), while 15 patients showed from six to nine missing teeth (0.4%). The analysis showed 363 cases of agenesis in the upper arch (0.64%) and 339 in the lower arch (0.60%). Unilateral (4.6%) and bilateral (4.4%) agenesis demonstrated a similar frequency. The most common bilateral missing teeth were the mandibular second premolars (1.9%) and the maxillary lateral incisors (1.6%). The results of this study observed a higher prevalence of hypodontia compared to previous studies conducted on the Italian population. Thus, a detailed and careful radiographic examination was important in diagnosing one or more missing teeth. This could help plan the best possible treatments, both esthetically and functionally, for these patients.

Background/Objectives: This systematic review aimed to evaluate the effectiveness and predictability of lower incisor intrusion with clear aligners in permanent dentition, addressing one of the most challenging aspects of vertical tooth movement control in the mandibular anterior region. Methods: A comprehensive literature search was conducted across five databases (PubMed, Scopus, Embase, and Cochrane) according to PRISMA guidelines. Eight clinical studies fulfilled the eligibility criteria. Risk of bias was assessed using ROBINS-I, and certainty of evidence was graded with GRADE. Key outcomes included the amount of achieved versus planned intrusion, predictability, treatment protocols, use of auxiliaries, and patient-related factors such as age and compliance. Results: Reported mean intrusion values ranged from 0.4 to 1.5 mm, with predictability between 35% and 65%. The effectiveness of intrusion was influenced by the magnitude of planned movement, auxiliaries (e.g., attachments, elastics), refinement strategies, and patient-specific factors. Substantial heterogeneity was present in measurement methods (CBCT, cephalometry, digital models) and clinical protocols (aligner change intervals, refinement frequency), preventing meta-analysis. Seven of the eight studies were rated as having a serious risk of bias, and the overall certainty of evidence was moderate to low. Long-term outcomes and patient-centered measures were not adequately assessed. Conclusions: Within the limitations of the available evidence, lower incisor intrusion with clear aligners may be considered a feasible orthodontic option when supported by biomechanically informed clinical management. However, conclusions should be interpreted with caution due to heterogeneity, high risk of bias, and lack of long-term data. Further standardized studies with longer follow-up are required to strengthen reliability and clinical applicability.

Objectives: This study aimed to evaluate the reproducibility of artificial intelligence (AI) in identifying cephalometric landmarks, comparing its performance with manual tracing by an experienced orthodontist. Methods: A high-quality lateral cephalogram of a 26-year-old female patient, meeting strict inclusion criteria, was selected. Eighteen cephalometric landmarks were identified using the WebCeph software (version 1500) in three experimental settings: AI tracing without image modification (AInocut), AI tracing with image modification (AI-cut), and manual tracing by an orthodontic expert. Each evaluator repeated the procedure 10 times on the same image. X and Y coordinates were recorded, and reproducibility was assessed using the coefficient of variation (CV) and centroid distance analysis. Statistical comparisons were performed using one-way ANOVA and Bonferroni post hoc tests, with significance set at p < 0.05. Results: AInocut achieved the highest reproducibility, showing the lowest mean CV values. Both AI methods demonstrated greater consistency than manual tracing, particularly for landmarks such as Menton (Me) and Pogonion (Pog). Gonion (Go) showed the highest variability across all groups. Significant differences were found for the Posterior Nasal Spine (PNS) point (p = 0.001), where AI outperformed manual tracing. Variability was generally higher along the X-axis than the Y-axis. Conclusions: AI demonstrated superior reproducibility in cephalometric landmark identification compared to manual tracing by an experienced operator. While certain points showed high consistency, others—particularly PNS and Go—remained challenging. These findings support AI as a reliable adjunct in digital cephalometry, although the use of a single radiograph limits generalizability. Broader, multi-image studies are needed to confirm clinical applicability.