Osteoporosis may alter mandibular bone structure and peri-implant remodeling, but it remains unclear whether such changes are detectable on dental radiographs using deep learning. This retrospective study evaluated whether osteoporosis can be discriminated in two mandibular regions of interest: peri-implant bone and the mental foramen region. Digital periapical radiographs acquired between November 2012 and October 2024 were analyzed in 51 women, including 25 patients with osteoporosis and 26 non-osteoporotic controls without a documented history or diagnosis of osteoporosis; the osteoporosis group was significantly older than the control group. Two binary classification experiments were performed using patient-level fivefold grouped cross-validation. The peri-implant experiment included 1682 cropped images and used an image-plus-metadata ResNet-18 model incorporating the time interval between implant placement and radiograph acquisition. The mental foramen experiment included 102 cropped images and used an image-only ResNet-18 model. Mean accuracy, F1 score, and area under the receiver operating characteristic curve were 0.613, 0.628, and 0.713 for the peri-implant region of interest (ROI) and 0.701, 0.713, and 0.744 for the mental foramen ROI, respectively. Both experiments showed substantial fold-to-fold variability. These findings suggest that ROI selection influences model behavior, but neither approach yielded sufficiently stable ROI-level classification performance under patient-level grouped validation to support individual patient-level screening claims. Nondiscriminatory AI results should therefore be interpreted as limited evidence under the present experimental conditions rather than as proof of radiographic equivalence.

Background: Cone-beam computed tomography (CBCT) is increasingly applied for the assessment of periodontal bone levels. However, its measurement reliability and consistency depend strongly on image quality parameters such as voxel size, noise, and reconstruction sharpness. With the growing use of CBCT datasets for artificial intelligence (AI)-based diagnostics, it is essential to understand how image degradation conditions affect examiner-derived measurement outcomes and the reliability of reference data used for AI training. Methods: An anonymized CBCT dataset containing one periodontally healthy tooth (31) and one tooth with pronounced periodontal bone loss (41) was analyzed. The original DICOM data were systematically degraded using controlled voxel enlargement (double and triple voxel size) and simulated image blur (Gaussian and median filtering). Six dentists (n = 6) independently performed standardized linear bone-level measurements, with three repeated measurements per tooth and image condition. Data were analyzed using the Shapiro–Wilk test for normality assessment, the Kruskal–Wallis H test for group comparisons, Bonferroni-adjusted Mann–Whitney U tests for post hoc pairwise comparisons, and intraclass correlation coefficients (ICC (2,1)) for inter-examiner reliability assessment. Results: A total of 180 measurements were evaluated. Image degradation conditions were associated with statistically significant differences in bone-level measurements for both teeth (tooth 31: p = 0.017; tooth 41: p = 0.0049). Significant pairwise differences were primarily observed between the original dataset and specific degraded conditions involving blur and reduced spatial resolution, while several comparisons remained non-significant. Inter-examiner reliability varied across image groups and decreased notably with pronounced voxel enlargement, particularly in the periodontally compromised tooth. Conclusions: Controlled image degradation conditions of CBCT image quality significantly affect measurement outcomes and inter-examiner reproducibility of periodontal bone measurements. These findings demonstrate that image quality is a critical determinant of measurement reliability and examiner-dependent interpretation. From both a clinical and AI-development perspective, maintaining adequate CBCT resolution may contribute to more consistent measurement behavior and more reliable training datasets.

Background: Traditional methods for palatal expansion using fixed appliances often face limitations in comfort and aesthetics. In comparison, aligner therapy has limitations, particularly regarding maxillary expansion. The aim of this study is to examine the biomechanical properties regarding the wire diameter and bending of different stainless steel wires to evaluate their potential for incorporation into maxillary aligner therapy. Materials and Methods: Three rectangular stainless steel wires (0.016″ × 0.022″, 0.017″ × 0.025″, and 0.019″ × 0.025″) were tested for mechanical expansion forces in the intermolar region, comparing non-tooth-shaped bent wires (A groups) and tooth-shaped bent wires (B groups). Using a Z010 testing machine (ZwickRoell GmbH and Co. KG, Ulm, Germany), expansion forces were measured at 1 mm intervals over a 5 mm distance, with 15 samples analyzed per group. Statistical analyses included the Shapiro–Wilk test for normal distribution, the Mann–Whitney U test, which revealed significant results (U = 225, p < 0.001), and the Kruskal–Wallis test, which indicated significance (H = 39.130; df = 2; p < 0.001). Results: Tooth-shaped bent wires exhibited significantly lower expansion forces than non-tooth-shaped bent wires for all tested wire types. This difference was most notable in wires with larger transverse profiles (0.019″ × 0.025″), where the tooth-shaped bent wires displayed a marked reduction in mechanical load capacity. Specific force measurements for non-tooth-shaped wires ranged from 760.61 ± 79.51 mN at 1 mm of deformation to 2468.46 ± 66.27 mN at 5 mm of deformation, while tooth-shaped wires ranged from 116.80 ± 3.74 mN to 1979.49 ± 23.23 mN. Conclusions: These findings suggest that non-tooth-shaped bent wires offer a more efficient and uniform expansion potential for maxillary movements due to their stable elastic properties. Clinically, integrating non-tooth-shaped stainless steel wires into aligner therapy may provide a viable method for maxillary expansion, supporting both first- and second-order movements in orthodontic treatment. Further research is needed to explore the integration of such wires for effective maxillary expansion in aligner therapy.

Smartphone-based augmented reality (AR) may help dentists visualize an implant axis during treatment or training. Before a virtual overlay can be displayed, however, the application must first recognize the real target reliably. This in vitro study tested how five lighting conditions affected recognition of a two-colored, 3D-printed polylactic acid (PLA) dental model by a Unity/Vuforia smartphone application. The application ran on a Samsung Galaxy S22. A GoPro HERO10 recorded the smartphone display at 240 frames per second, and the videos were evaluated frame by frame. Red, green, blue, daylight-like light-emitting diode (LED) and halogen illumination were tested at a central and an eccentric model position, with ten repetitions per condition and position. The model was not recognized under red or blue illumination within the predefined observation interval. Recognition was successful in all repetitions under green, daylight-like and halogen illumination. Recognition speed differed among these successful conditions, and the eccentric position delayed recognition under daylight-like and halogen illumination. A supplementary region-of-interest analysis also showed lighting-dependent differences in image brightness and contrast. These findings indicate that lighting should be standardized and documented when smartphone-based dental AR systems are developed and tested.