Search Results (3)

Objective: This retrospective cross-sectional radiographic study evaluated panoramic infrazygomatic crest (IZC) length and compared values according to skeletal class and sex. The term panoramic IZC length was used to describe a two-dimensional linear measurement obtained on panoramic radiographs, rather than a three-dimensional bone corridor for miniscrew insertion. Methods: A total of 180 archived digital panoramic radiographs of patients aged 16–30 years were retrospectively collected and grouped equally by skeletal class and sex. Skeletal class was determined using previously recorded lateral cephalometric tracings based on ANB angle criteria: Class I, 0–4°; Class II, >4°; and Class III, <0°. Panoramic IZC length was measured bilaterally on calibrated panoramic images using ImageJ, and the bilateral mean was used for subgroup comparisons. Group differences were analyzed using one-way ANOVA, independent-samples t-tests, and two-way ANOVA. Results: Panoramic IZC length differed significantly across skeletal classes, with the highest values found in Class III and the lowest values found in Class II (p < 0.001). Mean panoramic IZC length was 5.81 ± 0.38 mm in Class III females and 6.16 ± 0.41 mm in Class III males, compared with 4.99 ± 0.36 mm in Class II females and 5.51 ± 0.40 mm in Class II males. Males showed significantly greater values than females (p < 0.01). The sex-by-class interaction was significant (p = 0.04). Intra-observer repeatability was excellent (ICC = 0.95). Conclusions: Panoramic IZC length varies according to skeletal class and sex, with higher values in Class III individuals and males. These findings provide preliminary comparative morphometric information from routinely available panoramic radiographs. However, panoramic IZC length should not be interpreted as a three-dimensional insertion corridor, and CBCT remains necessary when comprehensive anatomical planning, sinus proximity assessment, or extra-alveolar miniscrew trajectory evaluation is required. Full article

Background: This narrative review synthesizes evidence on AI for orthodontic malocclusion diagnosis across five imaging modalities and maps diagnostic metrics to validation tiers and regulatory readiness, with focused appraisal of Class III detection (2019–2025). Key algorithms, datasets, clinical validation, and ethical/regulatory considerations are synthesized. Methods: PubMed, Scopus, and Web of Science were searched for studies published January 2019–October 2025 using (“artificial intelligence”) AND (“malocclusion” OR “skeletal class”) AND “cephalometric.” Records were screened independently by two reviewers, with disagreements resolved by consensus. Eligible studies reported diagnostic performance (accuracy, area under the receiver operating characteristic curve (AUC), sensitivity/specificity) or landmark-localization error for AI-based malocclusion diagnosis. Data on dataset size and validation design were extracted; no formal quality appraisal or risk-of-bias assessments were undertaken, consistent with a narrative review. Results: Deep learning models show high diagnostic accuracy: cephalogram classifiers reach 90–96% for skeletal Class I/II/III; intraoral photograph models achieve 89–93% for Angle molar relationships; automated landmarkers localize ~75% of points within 2 mm. On 9870 multicenter cephalograms, landmarking achieved 0.94 ± 0.74 mm with ≈89% skeletal-class accuracy when landmarks fed a classifier. Conclusion: AI can reduce cephalometric tracing time by ~70–80% and provide consistent skeletal classification. Regulator-aligned benchmarks (multicenter external tests, subgroup reporting, explainability) and pragmatic open-data priorities are outlined, positioning AI as a dependable co-pilot once these gaps are closed. Full article

Objectives: Knowledge of tooth axes is important in orthodontics; however, using just one method for evaluation, e.g., orthopantomograms for tip, is not highly reliable. This study aimed to investigate tooth axes in skeletal class I/II/III using two- and three-dimensional evaluations. Methods: In this retrospective study, lateral cephalometric radiographs, orthopantomograms and digitized models of 107 adolescent patients (Ø 13.5 years; n = 36/33/38 with cI/cII/cIII) prior to orthodontic treatment were analyzed digitally regarding tip and torque of teeth. Statistical analysis was performed using SPSS (p ≤ 0.05), G*power and a multiple testing tool (Bonferroni–Holm/Hochberg). Results: Dental compensation of skeletal cII/cIII was significant acc. to Bonferroni–Holm/Hochberg for the following variables: overjet compensation in cII was seen by more retroinclined upper incisors in cII by −5.9°/−5.3° and by −8.8°/−6.6° (U1-SN/U1-PP) vs. cI/cIII (effect size f = 0.489/0.446, power 0.996/0.988). In cIII, the lower incisors were more retroinclined by −8.5°/−10.9° (L1-MP) vs. cI/cII (f = 0.576, power 1.000) and by −8.5°/−8.9° and −6.0°/−7.0° (three-dimensional analysis: L1/L2) vs. cI/cII (f = 0.522/0.527, power 0.999). Compensation of distal occlusion was found by mesial tipping of L3 by 3.5° in cII (f = 0.242, power 0.591) vs. cIII. CIII showed transversal compensation by buccal tipping of the U5 by 5.9°/4.6° vs. cII/I (f = 0.355, power 0.910) and lingual tipping of L3 by −6.4° vs. cII and −3.8° vs. cI (f = 0.446, power 0.988) and L4 by −4.0°/−2.6° vs. cII/I (f = 0.326, power 0.846). Conclusions: Decompensation, e.g., uprighting of distal tipped canines, and further protrusion of incisors might not be desired in orthodontic treatment of adolescents. Full article