Background/Objectives: The early and accurate identification of impacted teeth in the maxilla is critical for effective dental treatment planning. Traditional diagnostic methods relying on manual interpretation of radiographic images are often time-consuming and subject to variability. Methods: This study presents a deep learning-based approach for automated classification of impacted maxillary canines using panoramic radiographs. A comparative evaluation of four pre-trained convolutional neural network (CNN) architectures—ResNet50, Xception, InceptionV3, and VGG16—was conducted through transfer learning techniques. In this retrospective single-center study, the dataset comprised 694 annotated panoramic radiographs sourced from the archives of a university dental hospital, with a mildly imbalanced representation of impacted and non-impacted cases. Models were assessed using accuracy, precision, recall, specificity, and F1-score. Results: Among the tested architectures, VGG16 demonstrated superior performance, achieving an accuracy of 99.28% and an F1-score of 99.43%. Additionally, a prototype diagnostic interface was developed to demonstrate the potential for clinical application. Conclusions: The findings underscore the potential of deep learning models, particularly VGG16, in enhancing diagnostic workflows; however, further validation on diverse, multi-center datasets is required to confirm clinical generalizability.

Background/Objectives: Deep learning models for cephalometric landmark detection report near-ceiling accuracy on single benchmarks, yet most are trained and tested on the same dataset. Whether the best in-domain architecture remains best out-of-domain has not been systematically quantified. Methods: Four architecture families (heatmap CNN, two-stage cascade, coordinate-regression Vision Transformer, pretrained ResNet-50) were each trained on two independently sourced datasets—ISBI 2015 (400 images, one device) and Aariz (1000 images, seven devices)—and evaluated on both, over their 19 shared landmarks in millimeters. All axes used three seeds (mean ± SD): the cross-dataset matrix, leave-one-device-out shift, balanced joint training, a pretrained-versus-scratch ablation, and a landmark-level breakdown. Results: In-domain mean radial error (MRE) was 2.47–3.04 mm (Aariz) and 4.12–5.55 mm (ISBI); cross-dataset error rose steeply (Generalization Drop—the relative increase in error out-of-domain—155–426%). The most accurate model in-domain (a from-scratch heatmap CNN, 2.47 mm) showed the largest drop, and every pretrained estimate fell below every from-scratch estimate (family means 248% vs. 380%): in-domain ranking did not predict cross-domain ranking. Leave-one-device-out revealed reproducible device-specific shift (held-out MRE 1.89–7.94 mm). Inter-observer variability was 0.53 mm, so cross-domain errors were 15–44× the human band. Balanced joint training reduced the cross-domain gap for all four architectures (both domains ≈ 2.1–3.5 mm) without harming in-domain accuracy. Pretraining more than halved cross-domain error from Aariz to ISBI (7.73 vs. 16.36 mm) but not in reverse, supporting the mechanism in one direction rather than uniformly. A-point, B-point, Nasion, and Menton all exceeded the 2 mm clinical threshold out-of-domain, including points localized to sub-millimeter accuracy in-domain. Conclusions: Single-dataset accuracy substantially overstates clinical generalizability, and the best in-domain architecture is not the most transferable, so in-domain leaderboards are an unreliable basis for selecting a model for clinical deployment; balanced multi-source training recovers most of the loss across the architectures tested.

The Effects of Light and Vibration on the Correction of Lower Incisor Crowding with Aligners - Turkish Journal of Orthodontics

Background/Objectives: Large language models (LLMs) are consulted for clinical decision support, and their reliability is judged by hallucination prevalence—a measure treating every unsupported element as equivalent, although a fabricated citation and a fabricated protocol differ in what a clinician acting on them would do. We tested whether prevalence tracks clinical risk. Methods: Five LLMs answered a 100-item orthodontic benchmark validated by three external orthodontists (content validity index 0.923). The reference standard was fixed by construction for the 45 items naming a non-existent entity, so any substantive elaboration is unsupported by design; citations were adjudicated against PubMed and CrossRef. Responses were coded with a seven-category taxonomy and assigned to clinical, operational, or epistemic severity tiers in a post hoc exploratory stratification, pre-specified rather than prospectively registered. Because all models answered the same items, comparisons used Cochran’s Q with pairwise McNemar tests and generalised estimating equations clustered on item. Results: Of 500 responses, 449 (89.8%) contained a hallucination but only 65 (13.0%; 95% CI 10.3–16.2) were clinically consequential: prevalence was roughly sevenfold greater than the rate of clinically consequential output as the authors defined it. Between-model differences were large for undifferentiated prevalence (73–100%; Q = 50.51, p < 0.001) and contracted at the clinical tier (10–16%; Q = 10.00, p = 0.040), where no pairwise contrast survived adjustment. Item-level clustering was far stronger for clinically consequential output than for undifferentiated prevalence (intra-class correlation 0.83 versus 0.07). Conclusions: Prevalence and clinically consequential error are not interchangeable and rank models differently. The stratification is exploratory and author-defined, and the benchmark stress-tests susceptibility to fabricated premises rather than surveying natural use.

Direct-print aligners are widely presented as the environmentally preferable alternative to thermoforming, but which part of the process governs the carbon released during manufacture has not been established. Four commercial printer–resin workflows were modelled: Graphy Tera Harz resin on a Uniz NBEE (A) and an Asiga MAX 2 (B), the LuxCreo iLux Pro as a closed chain (C), and Graphy resin on a DentaFab Sega Pro (D). Energy, material and throughput data came from the manufacturers. Production-phase CO₂e per aligner was propagated through 10,000 Monte Carlo draws, with the resin emission factor sampled across its full published range (3.0–6.5 kg CO₂e/kg) and the thermoformed comparator represented as a distribution. Mean footprints were 0.043, 0.036, 0.089 and 0.020 kg CO₂e per aligner (95% uncertainty intervals were 0.032–0.056, 0.026–0.047, 0.077–0.101 and 0.016–0.025), against a thermoformed median of 0.190. Direct printing was the lower of the two in every paired iteration. Resin dominated, contributing 41–79% of the total; solvent added a further 41% in the one workflow using it, while electricity of all kinds accounted for 19–36%. Thermoforming would match direct printing only at series lengths of 45 to 202 aligners, far above the 14–30 of clinical practice; under the most unfavourable resin factor the margin narrowed but did not close. Material efficiency, not energy sourcing, is where reductions are available.

Background/Objectives: Mandibular cortical morphology has been associated with vertical facial patterns, but differences in standardized radiomorphometric indices remain unclear. This study compared five mandibular indices among vertical skeletal patterns using CBCT-derived panoramic reconstructions. Methods: This retrospective cross-sectional study included 181 adults classified as hypodivergent (n = 42), normodivergent (n = 67), or hyperdivergent (n = 72) according to the sella–nasion-to-mandibular-plane angle. Mental (MI), panoramic mandibular (PMI), gonial (GI), antegonial (AI), and mandibular cortical (MCI) indices were assessed by a blinded dentomaxillofacial radiologist, and bilateral measurements were averaged. Continuous indices were compared using ANOVA or Welch ANOVA and ANCOVA adjusted for age and sex, and the MCI using the chi-square test and logistic regression. p values across the five indices were corrected using the Holm method. Results: No index differed significantly among the groups after correction for multiple comparisons. The MI showed the most notable nominal between-group difference (hypodivergent, 3.92 ± 0.73 mm; normodivergent, 3.73 ± 0.60 mm; hyperdivergent, 3.61 ± 0.68 mm). After adjustment for age and sex, the MI was 0.35 mm greater in hypodivergent than in hyperdivergent subjects (95% CI, 0.04–0.66), but the overall group effect did not remain significant after correction (p = 0.024; Holm p = 0.118). The unadjusted association between the MCI and vertical skeletal pattern (p = 0.035) did not remain significant after correction (Holm p = 0.174) or after adjustment. The PMI, GI, and AI showed no differences. Conclusions: After correction for multiple comparisons, vertical skeletal pattern was not significantly associated with any of the five radiomorphometric indices.