Background/Objectives: Early childhood caries (ECC) is a widespread and severe oral health problem that potentially affects the general health of children. Visual–tactile examination remains the diagnostic method of choice to diagnose ECC, although visual examination could be automated by artificial intelligence (AI) tools in the future. The aim of this study was the external validation of a recently published and freely accessible AI-based model for detecting ECC and classifying carious lesions in dental photographs. Methods: A total of 143 anonymised photographs of anterior deciduous teeth (ECC = 107, controls = 36) were visually evaluated by the dental study group (reference test) and analysed using the AI-based model (test method). Diagnostic performance was determined statistically. Results: ECC detection accuracy was 97.2%. Diagnostic performance varied between carious lesion classes (noncavitated lesions, greyish translucency/microcavity, cavitation, destructed tooth), with accuracies ranging from 88.9% to 98.1%, sensitivities ranging from 68.8% to 98.5% and specificities ranging from 86.1% to 99.4%. The area under the curve ranged from 0.834 to 0.964. Conclusions: The performance of the AI-based model is similar to that reported for the internal dataset used by developers. Further studies with independent image samples are required to comprehensively gauge the performance of the model.

The periodontal ligament (PDL) is crucial for maintaining the integrity and functionality of tooth-supporting structures. Mechanical forces applied to the tooth during orthodontic tooth movement generate pore pressure gradients, leading to interstitial fluid movement within the PDL. The generated fluid shear stress (FSS) stimulates the remodeling of PDL and alveolar bone. Herein, we present the construction of a parallel fluid-flow apparatus to determine the effect of FSS on PDL cells. The chamber was designed and optimized using computer-aided and computational fluid dynamics software. The chamber was formed by PDMS using a negative molding technique. hPDLCs from two donors were seeded on microscopic slides and exposed to FSS of 6 dyn/cm² for 1 h. The effect of FSS on gene and protein expression was determined using RT-qPCR and Western blot. FSS upregulated genes responsible for mechanosensing (FOS), tissue formation (RUNX2, VEGFA), and inflammation (PTGS2/COX2, CXCL8/IL8, IL6) in both donors, with donor 2 showing higher gene upregulation. Protein expression of PTGS2/COX2 was higher in donor 2 but not in donor 1. RUNX2 protein was not expressed in either donor after FSS. In summary, FSS is crucial in regulating gene expression linked to PDL remodeling and inflammation, with donor variability potentially affecting outcomes.

To compare the two-body wear under thermomechanical loading, three-body wear, degree of conversion and Vickers hardness of three permanent 3D-printed restorative materials with a milled composite control material. Three 3D-printed materials (Deltamed 3Delta Crown, Saremco Print Crowntec, Detax Freeprint Crown) and one milled composite (Voco Grandio Disc) were evaluated. For two-body wear, specimens were loaded against 5-mm Al₂O₃ antagonists at 50 N and 1.2 Hz with thermocycling between 5 °C and 55 °C and were scanned after 100,000, 300,000, and 500,000 cycles. For three-body wear, ACTA wear testing was performed in a millet-seed slurry at 15 N and 15% slip, with scanning after 50,000, 100,000, 150,000, and 200,000 cycles. Vickers hardness was measured by microindentation, degree of conversion by Raman spectroscopy, and surface morphology qualitatively by scanning electron microscopy. Wear increased progressively with cycle number in both wear models. In two-body wear, Saremco Print Crowntec showed the highest wear, whereas lower wear values were observed for Deltamed 3Delta Crown, Detax Freeprint Crown, and Grandio Disc. In three-body wear after 200,000 cycles, Grandio Disc showed the lowest wear, followed by Deltamed 3Delta Crown, whereas Saremco Print Crowntec and Detax Freeprint Crown showed the highest values. Grandio Disc showed significantly higher hardness than all 3D-printed materials, whereas no significant differences were detected among the printed materials. The degree of conversion of the 3D-printed materials ranged from 67.53 ± 0.53% to 71.90 ± 1.20%, with no significant differences among groups. Volume loss and hardness varied significantly by material. Here was no significant difference in DC. Filler content and distribution were the dominant factor influencing wear, followed by hardness and degree of conversion.

Objective: To develop realistic training teeth composed of multi‐material colours and gradients and evaluate them in comparison with the standard model and extracted teeth with a group of students. Material and Method: Three different teeth were virtually designed by use of multiple STL‐compartments and additively manufactured with different material gradients like colour, hardness and functional properties in a single printing process using MultiJet technology. The teeth included simulated hard‐/softissues and restorative materials like enamel, dentin, pulp, carious dentin, composite, amalgam, and gutta‐percha. The selected teeth were tested by a group of 25 clinical students in a volunteer hands‐on course. They had experience in caries removal, post insertion and preparation on real patients. Study procedures included the removal of a faulty direct and indirect restoration, of gutta‐percha as well as of carious dentin. The properties of the printed teeth for each task were assessed by the students using grades (1 = very good, 2 = good, 3 = satisfactory, 4 = sufficient, 5 = poor). Conventional model teeth and extracted real teeth served as reference. Results: In comparison to standard model teeth, printed teeth were rated 1.1 ± 0.7, with a grade of 2.4 ± 1.2 for haptic impression and 1.2 ± 0.8 for realistic perception of the exercise. In comparison to extracted teeth, the colour of the enamel(2.1 ± 1.4), the dentin(1.8 ± 1.3) and the carious lesion(1.2 ± 0.8) were evaluated with overall good or very good values. The new features were rated with 1.2 ± 0.7 for the Amalgam filling, 1.0 ± 0.3 for the caries lesion, 1.4 ± 1.5 for the crown, and 2.0 ± 1.0 for the gutta‐percha. Conclusion: The results of the pilot study confirm the potential of multi‐material additive manufacturing for educational purposes. Students preferred printed teeth in comparison with conventional acrylic and extracted teeth, considering the simulation of a training scenario close to clinical reality.