Maxillary second molars with root fusion exhibit considerable anatomical variability that complicates dental treatment planning. Cone-beam computed tomography (CBCT) has become an essential imaging modality for evaluating such complex morphology. This study aimed to develop a deep learning (DL)-based computer-aided diagnosis (CAD) system for the automated classification of root fusion types in maxillary second molars, and to systematically evaluate the impact of image preprocessing strategies on model performance. CBCT images from 172 maxillary second molars (3188 two-dimensional slices) were collected and classified into seven root fusion types. A ResNet50 convolutional neural network was trained with tooth-level 5-fold cross-validation. The effects of grayscale bit depth (4096 vs. 256 levels), preprocessing mode (original, binarized, and segmented images), and contrast enhancement techniques (histogram equalization [HE] and contrast-limited adaptive histogram equalization [CLAHE] with varying parameters) on classification accuracy were systematically compared. The model trained on segmented, non-binarized images with 4096 grayscale levels achieved the highest overall accuracy of 91.6%. In descending order of performance: CLAHE-1 (90.5%), CLAHE-3 (90.5%), CLAHE-40 (88.2%), and HE (83.9%). The unprocessed original image group yielded the lowest accuracy (57.5%). A ResNet50-based DL system can effectively classify complex root fusion morphology in maxillary second molars. Preserving the natural radiopacity gradient of CBCT images, rather than applying artificial contrast enhancement, was critical for optimal model performance. These findings provide a reference for preprocessing strategy selection in dental CBCT-based AI systems.
Background: Implant macrogeometry and bone density are major determinants of primary stability. This study aimed to evaluate primary stability across different implant macrodesigns and block models. Materials and Methods: A total of 153 implants were divided into Group 1 (AnyRidge), Group 2 (BlueDiamond RC), and Group 3 (BlueDiamond NC) (n = 51 each). Each implant was sequentially tested in synthetic D1–D4 bone models. Insertion torque values (ITV) and implant stability quotient (ISQ) were recorded. Results: In D1, ITV was highest in Group 1 (69.51 ± 1.50 Ncm), followed by Group 2 (53.82 ± 6.75 Ncm) and Group 3 (49.31 ± 7.00 Ncm) (p < 0.01, p < 0.001). In D4, ITV decreased to 34.71 ± 3.23, 19.90 ± 3.08, and 11.18 ± 2.14 Ncm, respectively (p < 0.001). D1 ISQ values were 84.53 ± 2.51, 72.08 ± 3.69, and 66.33 ± 2.63, respectively (p < 0.001). In D4, ISQ values were 60.29 ± 3.84, 52.86 ± 3.73, and 52.18 ± 2.48, respectively (p < 0.001); Groups 2 and 3 did not differ significantly. Conclusions: Implant macrodesign influences primary stability, with AnyRidge demonstrating the highest ITV and ISQ values in D1 as well as D4. Further clinical studies would be beneficial to support these findings.
Skeletal muscle xenograft models provide valuable platforms for studying human muscle regeneration and evaluating cell-based therapeutic strategies. However, variability in xenograft outcomes remains a major challenge, limiting the reproducibility and predictive value of human muscle regeneration models. Systematic optimization of parameters is essential to establish robust and reliable platforms. In this study, we optimized key parameters influencing human skeletal muscle xenograft regeneration using human iPSC-derived skeletal myogenic progenitors generated through teratoma differentiation. We first evaluated the effects of extracellular matrix (ECM) substrates on the in vitro culture of these myogenic progenitors, finding that ECM influences their myogenic potential. We next systematically optimized host preconditioning parameters by evaluating irradiation dose and the timing of cardiotoxin-induced muscle injury prior to transplantation. Then, using an optimized xenograft preconditioning platform, we evaluated ECM-assisted cell delivery strategies, finding that ECM co-delivery can improve donor cell retention, engraftment, and donor-derived muscle regeneration compared with cell transplantation alone. Together, these findings demonstrate that optimization of both host preconditioning and cell delivery conditions is critical for establishing reproducible human skeletal muscle xenograft models. This optimized platform enables improved evaluation of human myogenic progenitor regenerative potential and provides a framework for advancing cell-based approaches for skeletal muscle repair.
Background/Objectives: Reproducing enamel optical behavior requires appropriate refractive and translucency characteristics. This in vitro study compared the effective group refractive index (RI) and visible translucency of five resin composites with human enamel and assessed whether the two optical properties corresponded. Methods: Sixty 1 mm specimens were evaluated across six groups (n = 10 each): Filtek Easy Match, Tetric EvoCeram, Estelite Sigma Quick, Charisma Diamond ONE, Harmonize, and human enamel. Effective group RI was measured at 1310 nm using optical coherence tomography. CIELAB coordinates were recorded over white and black backgrounds, and CIEDE2000 (TP00) and CIELAB (TPaβ) translucency parameters were calculated. Heteroscedasticity-robust models with Holm-adjusted composite-versus-enamel contrasts were used. Results: Enamel had a mean RI of 1.628 and TP00 of 15.76. All composites had lower RI values than enamel (adjusted p < 0.001). TP00 did not differ significantly from enamel for Filtek Easy Match, Tetric EvoCeram, or Charisma Diamond ONE, whereas Estelite Sigma Quick and Harmonize had lower TP00 values (adjusted p = 0.012 and 0.018). RI was not independently associated with TP00 after adjustment for material (p = 0.352). Conclusions: None of the composites reproduced enamel RI, while visible translucency was material-dependent and was not explained by effective group RI alone.
Improvements in early biological responses to engineered dental implant surfaces have not consistently translated into superior long-term survival, marginal bone stability, or prevention of peri-implant disease relative to contemporary surfaces. This narrative review focuses on titanium implants and abutments, examining six complementary strategies: osseointegration, soft-tissue integration, antibacterial activity, osteoimmunomodulation, periodontal ligament regeneration, and multifunctional and smart surfaces. It evaluates their biological rationale alongside the preclinical and clinical evidence supporting their use. Experimental studies demonstrate changes in cell attachment, bone formation, microbial control, and immune regulation, while responsive systems coordinate these functions through controlled release or activation. Most emerging approaches remain supported predominantly by preclinical evidence. Future development should match surface functions to the requirements of each implant region and healing stage. Clinical translation requires reproducible performance and comparative studies demonstrating benefits in patient-relevant outcomes, including loading safety, tissue stability, and long-term maintainability.
