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The optimal configuration of a customized implant abutment is crucial for bone remodeling and is influenced by various design parameters. This study introduces an optimization process for designing two-piece zirconia dental implant abutments. The aim is to enhance bone remodeling, increase bone density in the peri-implant region, and reduce the risk of late implant failure. A 12-month bone remodeling algorithm subroutine in finite element analysis to optimize three parameters: implant placement depth, abutment taper degree, and gingival height of the titanium base abutment. The response surface analysis shows that implant placement depth and gingival height significantly impact bone density and uniformity. The taper degree has a smaller effect on bone remodeling. The optimization identified optimal values of 1.5 mm for depth, 35° for taper, and 0.5 mm for gingival height. The optimum model significantly increased cortical bone density from 1.2 to 1.937 g/cm 3 in 2 months, while the original model reached 1.91 g/cm 3 in 11 months. The standard deviation of density showed more uniform bone apposition, with the optimum model showing values 2 to 6 times lower than the original over 12 months. The cancellous bone showed a similar trend. In conclusion, the depth and taper have a significant effect on bone remodeling. This optimized model significantly improves bone density uniformity.

The removal of subgingival deposits, especially calculus, plays a crucial role in basic periodontal therapy. However, manual detection methods affect accuracy owing to the operator’s skill. To avoid this uncertainty, we have developed a calculus detection device named “Sensor probe” and evaluated its ability to detect calculus for future clinical applications. The Sensor probe consisted of a 635 nm-wavelength semiconductor laser and a 0.5 mm-diameter single-mode optical fiber. Initially, the performance of the device was evaluated using clinically obtained extracted teeth with calculus covered with a stainless-steel shielding plate with pinhole. Then, the effect of the optical fiber’s end shape on calculus detection performance was analyzed. Lastly, the performance of the Sensor probe was compared to that of a conventional periodontal probe in terms of accuracy, sensitivity, and specificity for calculus detection using calculus-covered extracted teeth. The results indicated that Sensor probe detected dental calculus through the pinhole with a diameter of 300 μm or more when applied from a distance of 100 μm. The results analyzing the effect of the optical fiber’s end shape on calculus detection performance showed that cutting the fiber end at an angle of 45° resulted in the most effective calculus detection. This may be because the laser light refracted on the cut surface and concentrated on the fiber side. Moreover, by comparing the performance of this device to a conventional periodontal probe revealed that the Sensor probe showed improved calculus detection accuracy in deeper periodontal pockets. This improvement was particularly significant in the apical third, where detection is typically difficult. In conclusion, a Sensor probe that uses an optical fiber with a 45° angled end may facilitate subgingival calculus detection. In future clinical applications, Sensor probes could lead to more accurate and efficient calculus removal, especially for deeper periodontal pockets.

The aim of this study was to investigate the bactericidal effect and recovery of biocompatibility of contaminated titanium surfaces using a combination treatment involving silver, copper, or iron ion application along with 400 nm purple-LED light irradiation. Additionally, the study sought to develop a functional calcium phosphate (CaP) coating treatment on titanium surfaces following disinfection, to promote re-osseointegration. A purple-LED emitting light at 400 nm was utilized to irradiate Staphylococcus aureus suspensions and biofilms in the presence of various concentrations of silver, copper, and iron solutions for 1 min. The bactericidal effect and electron spin resonance (ESR) spectrum were subsequently evaluated. Additionally, the hydrophilicity of the titanium surface and cell viability of MC3T3-E1 cells after combination treatment with silver ion was evaluated. Furthermore, a titanium surface coating with CaP gene transfection carrier containing plasmid DNA was developed using an electric current. The activity of hard tissue formation was then evaluated both in vitro and in vivo post-treatment. The bactericidal effect of the combination treatment with silver ions was attributed to the generation of hydroxyl radicals, whereas the effects from iron and copper treatments were not radical-mediated. The silver treatment significantly restored the hydrophilicity and cell affinity of the titanium surface. Moreover, CaP coating applied via an electric current (30 µA for 5 min) enhanced hard tissue formation activity on the titanium surface in both in vitro and in vivo settings. The combination treatment utilizing silver ions and purple-LED irradiation significantly enhanced bactericidal effects by generating high levels of hydroxyl radicals. Additionally, coating the titanium surface with functionalized CaP promoted early osseointegration, suggesting a promising strategy for improving implant outcomes.

This review aims to explore the application of AI (artificial intelligence) in fixed prosthodontics and implant-supported fixed restorations, with a specific focus on the accuracy, effectiveness, and clinical applicability of AI models for optimizing treatment planning and predicting clinical outcomes. This review followed the PCC (Population, Concept, Context) framework. A systematic search was conducted using PubMed, Scopus, and Embase for studies published between January 2010 and July 2025. Keywords included “artificial intelligence,”, “deep learning”, “digital dentistry”, “prosthodontic treatment planning,” “clinical decision support,” and “outcome prediction.” The initial databases search yielded 834 results. After selection, 20 studies were included for analysis in this review. AI applications were grouped into four domains: implant planning, crown design, full-arch framework optimization, and prognostic modeling. Convolutional neural networks (CNNs), generative adversarial networks (GANs), regression models, and optimization algorithms were most frequently employed. In implant planning, AI achieved high accuracies (90–99.5%) for site detection, drilling protocols, and bone assessment. Crown design studies demonstrated occlusal and morphological deviations within clinically acceptable thresholds (0.18–0.30 mm) and mean internal gaps of 59–83 μm, while reducing design time by up to fourfold compared with conventional workflows. For full-arch prosthetics, optimization methods such as particle swarm optimization (PSO) and bi-evolutionary structural optimization (BESO) enhanced efficiency and reduced stress concentration in simulated workflows. Prognostic modeling showed promising performance, with models achieving over 90% accuracy in predicting implant survival and treatment outcomes. AI applications in prosthodontics are most developed in implant site planning and crown design, with fewer studies on full-arch optimization and prognosis. Models generally achieve high accuracy and efficiency, but most evidence is early-stage and simulation-based. Future research should focus on prospective validation, multimodal integration, and patient-centered outcomes to ensure clinical reliability.

A limitation of widely used intraoral scanners (IOSs) is their inability to capture finish lines at the subgingival marginal area, as they only extract surface information. Swept-source optical coherence tomography (SS-OCT) captures high-speed, high-resolution cross-sectional images of soft and hard tissues. Integrating this technology can overcome clinical IOS limitations. Therefore, this study was conducted to fabricate crowns from three-dimensional images scanned with SS-OCT as a proof-of-principle for its application in IOSs and to evaluate fit accuracy. TRIOS3 was used for comparison, with both SS-OCT and TRIOS3 scanned three times, and crowns were fabricated using the same digital workflow. Internal gaps were measured using scanning electron microscopy, and marginal fit was evaluated via microscopy. Results showed that TRIOS3 had superior accuracy. SS-OCT can image solely in the occlusal direction, with accuracy decreasing at greater depths, which reduces precision around the margin. Additionally, SS-OCT lacks automatic correction of surface information in computer-aided design (CAD) software. To improve SS-OCT accuracy for abutment tooth measurements, automatic margin correction, improved CAD compatibility, and specialized probes for capturing tooth features are needed.

To investigate bone remodelling responses to mandibulectomy, a joint external and internal remodelling algorithm is developed here by incorporating patient-specific longitudinal data. The primary aim of this study is to simulate bone remodelling activity in the conjunction region with a fibula free flap (FFF) reconstruction by correlating with a 28-month clinical follow-up. The secondary goal of this study is to compare the long-term outcomes of different designs of fixation plate with specific screw positioning. The results indicated that the overall bone density decreased over time, except for the Docking Site (namely DS1, a region of interest in mandibular symphysis with the conjunction of the bone union), in which the decrease of bone density ceased later and was followed by bone apposition. A negligible influence on bone remodeling outcome was found for different screw positioning. This study is believed to be the first of its kind for computationally simulating the bone turn-over process after FFF maxillofacial reconstruction by correlating with patient-specific follow-up.

The biomechanics associated with buccal bone thickness (BBT) augmentation remains poorly understood, as there is no consistent agreement in the adequate BBT to avoid over-loading resorption or over-augmenting surgical difficulty. This study utilizes longitudinal clinical image data to establish a self-validating time-dependent finite element (FE)-based remodeling procedure to explore the effects of different buccal bone thicknesses on long-term bone remodeling outcomes in silico. Based upon the clinical computed tomography (CT) scans, a patient-specific heterogeneous FE model was constructed to enable virtual BBT augmentation at four different levels (0.5, 1.0, 1.5, and 2.0 mm), followed by investigation into the bone remodeling behavior of the different case scenarios. The findings indicated that although peri-implant bone resorption decreased with increasing initial BBT from 0.5 to 2 mm, different levels of the reduction in bone loss were associated with the amount of bone augmentation. In the case of 0.5 mm BBT, overloading resorption was triggered during the first 18 months, but such bone resorption was delayed when the BBT increased to 1.5 mm. It was found that when the BBT reached a threshold thickness of 1.5 mm, the bone volume can be better preserved. This finding agrees with the consensus in dental clinic, in which 1.5 mm BBT is considered clinically justifiable for surgical requirement of bone graft. In conclusion, this study introduced a self-validating bone remodeling algorithm in silico, and it divulged that the initial BBT affects the bone remodeling outcome significantly, and a sufficient initial BBT is considered essential to assure long-term stability and success of implant treatment.

Background Cognitive function plays a crucial role in human life, and its maintenance and improvement are essential in both young and older adults. Since cognitive decline can be associated with oral function decline, preventing the decline in both cognitive and oral functions is an urgent social issue. Several training methods to improve each function have been proposed. Previous studies have indicated that greater brain activity during training is associated with increased benefits for cognitive function. Although adding cognitive function elements to oral function training may promote the activation of brain activity during oral function training, the effects have not been validated. The main purpose of this study is to develop a novel training program that combines oral function training with cognitive training, which is expected to activate key brain regions involved in oral and cognitive functions, such as the left dorsolateral prefrontal cortex (DLPFC) and right medial prefrontal cortex (mPFC). Methods Four types of training programs combining oral and cognitive training: PaTaKaRa × calculation, lip exercise × N-back, tongue exercise × inhibition, and tongue exercise × memory, were developed. Each program had seven levels of difficulty [level 0 (no cognitive load) and level 6 (maximum difficulty)]. Twelve healthy young adults participated in the study and were instructed to perform all four programs. Brain activity in the left DLPFC and right mPFC were measured during each training session using two-channel near-infrared spectroscopy (NIRS). Results No significant brain activity was observed during training at level 0. Brain activity in the left DLPFC was significantly increased at levels 1 and 2 and in the left DLPFC and right mPFC at level 6 during PaTaKaRa × calculation training. Brain activity in the left DLPFC was significantly increased at level 6 during tongue exercise × inhibition training. Brain activity in the left DLPFC and right mPFC was significantly increased at level 6 during lip exercise × N-back training. Conclusion Oral function training did not significantly increase brain activity; nevertheless, oral function with cognitive training stimulated brain activity in the prefrontal cortex. Trial registration : UMIN-CTR. ID: UMIN000039678. date: 06/03/2020.

This study developed a new framework by correlating the clinical measurements in vivo with numerical modeling in silico at different time points for quantifying the magnitudes and directions of oral muscle forces. [Display omitted] The human masticatory system has received significant attention in the areas of biomechanics due to its sophisticated co-activation of a group of masticatory muscles which contribute to the fundamental oral functions. However, determination of each muscular force remains fairly challenging in vivo; the conventional data available may be inapplicable to patients who experience major oral interventions such as maxillofacial reconstruction, in which the resultant unsymmetrical anatomical structure invokes a more complex stomatognathic functioning system. Therefore, this study aimed to (1) establish an inverse identification procedure by incorporating the sequential Kriging optimization (SKO) algorithm, coupled with the patient-specific finite element analysis (FEA) in silico and occlusal force measurements at different time points over a course of rehabilitation in vivo; and (2) evaluate muscular functionality for a patient with mandibular reconstruction using a fibula free flap (FFF) procedure. The results from this study proved the hypothesis that the proposed method is of certain statistical advantage of utilizing occlusal force measurements, compared to the traditionally adopted optimality criteria approaches that are basically driven by minimizing the energy consumption of muscle systems engaged. Therefore, it is speculated that mastication may not be optimally controlled, in particular for maxillofacially reconstructed patients. For the abnormal muscular system in the patient with orofacial reconstruction, the study shows that in general, the magnitude of muscle forces fluctuates over the 28-month rehabilitation period regardless of the decreasing trend of the maximum muscular capacity. Such finding implies that the reduction of the masticatory muscle activities on the resection side might lead to non-physiological oral biomechanical responses, which can change the muscular activities for stabilizing the reconstructed mandible.

Frictional contact between biological tissues is of critical importance in biomechanics and clinical treatment strategies, which is particularly relevant to diarthrodial joints, where articular cartilage surfaces undergo reciprocal contact loading for thousands of cycles per day. Taking the temporomandibular joint (TMJ) as an example, mandibular resection and reconstruction significantly alter the masticatory system and impact its biomechanical conditions. Clinical evidence indicates that pain is more frequent in the contralateral TMJ after this kind of surgery. However, there has been limited analysis of TMJ biomechanics following reconstructive surgery to date. Therefore, our study aimed to investigate the effects of masticatory muscle loss on stress distribution in the TMJs, determine an optimum loading region to mitigate excessive stress in the contralateral TMJ, and explore how the frictional change influences the biomechanics of the TMJ. The results demonstrate that the loss of masticatory muscles on the ipsilateral side due to resection can increase contact pressure in the contralateral TMJ and that incisor and ipsilateral dental implant occlusal loading generates the most desired stress patterns in the contralateral TMJ. This study reveals that the excessive contact pressure could increase the real contact area in the joint and further cause a transition from fluid film lubrication to solid contact, leading to increased friction and wear. This work sheds some light on asymmetric anatomy and frictional condition changes arising from surgery, which contribute to stress concentration in the contralateral TMJ and may be associated with degenerative changes. These findings hold significant clinical implications for selecting an optimal and patient-specific occlusal loading to mitigate excessive contact pressure and potential damage in the articular joint.