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Objectives Nonsteroidal anti‐inflammatory drugs (NSAIDs) are often prescribed following the placement of dental implants, but the effects of these drugs on the osseointegration process are poorly understood. We designed a randomized, placebo‐controlled pilot study to quantitatively assess the effect of NSAIDs during early implant osseointegration. Materials and Methods Subjects receiving a maxillary dental implant were randomized to take naproxen or placebo for 7 days after the surgery. Implant osseointegration was quantified using Resonance Frequency Analysis device. Implant‐Stability‐Quotient (ISQ) measurement was performed at the time of surgery and at follow‐up visits 1, 4, and 16 weeks after surgery. Periapical radiographs were taken to measure the marginal bone level. Separately, a questionnaire of NSAIDs usage was provided to subjects presenting with early implant failure. Results After 4 weeks, ISQ values increased modestly ( + 1%) in subjects receiving naproxen whereas subjects receiving placebo had a much larger increase in ISQ value (+41%). We observed 55% more marginal bone loss at 4 weeks, and 52% at 16 weeks in the naproxen group compared to the placebo group. These results were not found to have statistically significant between groups (p ≥ 0.05). These effect sizes and variance were used to conduct a power analysis to determine the necessary sample size for future studies. Furthermore, our separate questionnaire study revealed that 68% of our patients with early failed dental implants reported a history of NSAIDs usage after the surgery. Conclusion In conclusion, this pilot study provides effect sizes and sample size estimates for future studies to definitively determine recommendations regarding NSAID usage following dental implant surgery. Nonetheless, our study did not observe any statistically significant differences in ISQ value or marginal bone loss after up to 16 weeks of follow‐up between subjects from naproxen and placebo groups.

Evaluation of the initial stability of implants is essential to reduce the number of implant failures of pedicle screws after orthopedic surgeries. Laser resonance frequency analysis (L-RFA) has been recently proposed as a viable diagnostic scheme in this regard. In a previous study, L-RFA was used to demonstrate the diagnosis of implant stability of monoaxial screws with a fixed head. However, polyaxial screws with movable heads are also frequently used in practice. In this paper, we clarify the characteristics of the laser-induced vibrational spectra of polyaxial screws which are required for making L-RFA diagnoses of implant stability. In addition, a novel analysis scheme of a vibrational spectrum using L-RFA based on machine learning is demonstrated and proposed. The proposed machine learning-based diagnosis method demonstrates a highly accurate prediction of implant stability (peak torque) for polyaxial pedicle screws. This achievement will contribute an important analytical method for implant stability diagnosis using L-RFA for implants with moving parts and shapes used in various clinical situations.

The paper aims to study the sensor that identifies the maturity of oil palm fruit bunches by using a flat-type inductive concept based on a resonant frequency technique. Conventionally, a human grader is used to inspect the ripeness of the oil palm fresh fruit bunch (FFB) which can be inconsistent and inaccurate. There are various new methods that are proposed with the intention to grade the ripeness of the oil palm FFB, but none has taken the inductive concept. In this study, the resonance frequency of the air coil is investigated. Samples of oil palm FFB are tested with frequencies ranging from 20 Hz to 10 MHz and the results obtained show a linear relationship between the graph of the resonance frequency (MHz) against time (Weeks). It is observed that the resonance frequencies obtained for Week 10 (pre-mature) and Week 18 (mature) are around 8.5 MHz and 9.8 MHz, respectively. These results are compared with the percentage of the moisture content. Hence, the inductive method of the oil palm fruit maturity sensor can be used to detect the change in water content for ripeness detection of the oil palm FFB.

Accurate assessment of dental implant stability is critical for predicting osseointegration outcomes and guiding clinical decision-making. Resonance frequency analysis (RFA) is a widely adopted non-invasive method for measuring implant stability quotient (ISQ); however, signal acquisition noise frequently compromises measurement reliability, leading to variable ISQ readings. This study aimed to develop and evaluate a deep learning-enhanced RFA framework integrating a denoising convolutional neural network (CNN) with a metadata-aware prediction network to improve ISQ estimation accuracy and signal quality. A retrospective dataset of 100 implants (300 signal samples; three acquisitions per implant) was analyzed. The framework comprised: (1) a denoising CNN to suppress signal contamination and improve signal-to-noise ratio (SNR), and (2) a metadata-aware prediction network estimating ISQ from denoised signals and implant-specific parameters (bone density category and insertion torque). Performance was evaluated on a held-out test set (20 implants, 60 samples) using MAE, RMSE, R , and tolerance accuracy within ±3 ISQ units, and compared against a traditional RFA baseline. The denoising network reduced noise by up to 85% and improved mean SNR from 12.3 dB to 22.8 dB. The proposed model achieved MAE of 1.85 ISQ, RMSE of 2.40 ISQ, R of 0.91, and tolerance accuracy of 92% within ±3 ISQ, outperforming the traditional baseline (MAE 2.65; RMSE 3.35; R 0.83; tolerance accuracy 77%). The deep learning-enhanced RFA framework substantially improved signal quality and ISQ prediction accuracy over traditional RFA methods, supporting its potential in clinical implant stability monitoring. The framework should currently be regarded as a proof-of-concept; multi-center prospective validation incorporating real-world noise profiling and clinical outcome assessment is required before clinical deployment.

Background and Objectives: The aim of this study was to clinically evaluate the primary and secondary stability of dental implants with different macrodesigns using resonance frequency analysis and to determine whether implant design and length influence implant stability. Materials and methods: This study included 48 healthy patients receiving dental implants, and a pre-implant planning protocol was used, which involved detailed bone analysis, clinical examinations, and Cone beam computed tomography (CBCT) analysis. The implants were of various types and dimensions (Alpha-Bio Tec (Israel), DFI, SPI, and NEO), and the surgical procedures were performed using standard methods. Implant stability was measured using resonance frequency analysis (RFA) immediately after placement and after 3, 6, and 12 months. The total number of implants placed in all patients was 96. Results: The average primary stability value for 10 mm SPI implants placed in the maxilla was 68.2 ± 1.7 Implant Stability Quotient (ISQ) units, while for 10 mm NEO implants, it was 74.0 ± 0.9. The average primary stability value for a 10 mm DFI implant placed in the mandible was 72.8 ± 1.2 ISQ, while for a 10 mm NEO implant placed in the mandible, it was 76.3 ± 0.8 ISQ. Based on the Friedman ANOVA test, the differences in the stability measurements for the 10 mm and 11.5 mm SPI implants and for the 10 mm and 11.5 mm NEO implants in the maxilla on day 0 and after 3, 6, and 12 months were significant at p < 0.05. Similarly, based on the Friedman ANOVA test, the differences in the stability measurements for the 10 mm and 11.5 mm DFI implants and for the 10 mm and 11.5 mm NEO implants in the mandible on day 0 and after 3, 6, and 12 months were significant at p < 0.05 (p = 0.00000). Conclusions: Universal tapered implants of the NEO type stood out as the optimal choice, as they provided statistically significantly higher primary stability in both soft and hard bone types compared to other implants. The implant length did not significantly affect this stability.

Implant stability plays a critical role for successful osseointegration. Successful osseointegration is a prerequisite for functional dental implants. Continuous monitoring in an objective and qualitative manner is important to determine the status of implant stability. Implant stability is measured at two different stages: Primary and secondary. Primary stability comes from mechanical engagement with cortical bone. Secondary stability is developed from regeneration and remodeling of the bone and tissue around the implant after insertion and affected by the primary stability, bone formation and remodelling. The time of functional loading is dependent upon the implant stability. Historically the gold standard method to evaluate stability were microscopic or histologic analysis, radiographs, however due to invasiveness of these methods and related ethical issues various other methods have been proposed like cutting torque resistance, reverse torque analysis, model analysis etc. It is, therefore, of an utmost importance to be able to access implant stability at various time points and to project a long term prognosis for successful therapy. Therefore this review focuses on the currently available methods for evaluation of implant stability.

Non-invasive objective implant stability measurements are needed to determine the appropriate timing of prosthetic fitting after implant placement. We compared the early implant stability results obtained using resonance frequency analysis (RFA) and damping capacity analysis (DCA) depending on the implant length and bone density. Total 60, 4.0 mm diameter implants of various lengths (7.3 mm, 10 mm, and 13 mm) were used. In Group I, low-density bone was described using 15 PCF (0.24 g/cm3) polyurethane bone blocks, and in Group II, 30 PCF (0.48 g/cm3) polyurethane bone blocks were used to describe medium density bone. RFA was performed using an Osstell ® Beacon+; DCA was performed using Anycheck ® . Measurements were repeated five times for each implant. Statistical significance was set at P <0.05. In Group I, bone density and primary implant stability were positively correlated, while implant length and primary implant stability were positively correlated. In Group II, the implant stability quotient (ISQ) and implant stability test (IST) values in did not change significantly above a certain length. Primary implant stability was positively correlated with bone density and improved with increasing implant length at low bone densities. Compared with the Osstell ® Beacon+, the simplicity of Anycheck ® was easy to use and accessible.

This study assessed initial stability using Resonance Frequency Analysis (RFA) and Damping Capacity Analysis (DCA) devices in three different bone conditions: a control group without bone defects, a cortical bone defect model, and a cancellous bone defect model. Additionally, the correlation between Implant Stability Quotient (ISQ) and Implant Stability Test (IST) values was investigated. This in vitro study utilized a controlled experimental design with bovine rib specimens. Thirty implants were placed in specimens divided into three groups ( n  = 10): no defect (Group A), cortical bone defect (Group B), and cancellous bone defect (Group C). Implant stability was measured using RFA and DCA devices. ISQ and IST values in Group A were 84.83 ± 1.38 and 86.52 ± 1.73, respectively; in Group B, they were 64.20 ± 4.87 and 64.25 ± 5.26, respectively; and in Group C, they were 82.80 ± 5.74 and 86.47 ± 4.01, respectively. Statistical analyses were performed using Kruskal-Wallis test with Bonferroni test to compare differences among groups, and Spearman’s correlation analysis was used to evaluate the relationship between ISQ and IST values. A significant reduction in ISQ( p  < 0.001) and IST( p  < 0.001) values was observed in the cortical bone defect group. In contrast, the cancellous bone defect group showed no significant difference from the control group (ISQ: p  = 0.493, IST: p  = 0.594). ISQ and IST values exhibited a significant positive correlation ( r  = 0.798, p  < 0.001), and stability values varied significantly with measurement direction. This study confirmed that cortical bone defects significantly reduced primary stability, whereas cancellous bone defects had no significant impact when cortical bone was sufficient. Additionally, ISQ and IST values showed a strong positive correlation, indicating that both methods provide consistent assessments of implant stability.

Accurate detection of implant loosening is crucial for early intervention in total hip replacements, but current imaging methods lack sensitivity and specificity. Vibration methods, already successful in dentistry, represent a promising approach. In order to detect loosening of the total hip replacement, excitation and measurement should be performed intracorporeally to minimize the influence of soft tissue on damping of the signals. However, only implants with a single sensor intracorporeally integrated into the implant for detecting vibrations have been presented in the literature. Considering different mode shapes, the sensor’s position on the implant is assumed to influence the signals. In the work at hand, the influence of the position of the sensor on the recording of the vibrations on the implant was investigated. For this purpose, a simplified test setup was created with a titanium rod implanted in a cylinder of artificial cancellous bone. Mechanical stimulation via an exciter attached to the rod was recorded by three accelerometers at varying positions along the titanium rod. Three states of peri-implant loosening within the bone stock were simulated by extracting the bone material around the titanium rod, and different markers were analyzed to distinguish between these states of loosening. In addition, a modal analysis was performed using the finite element method to analyze the mode shapes. Distinct differences in the signals recorded by the acceleration sensors within defects highlight the influence of sensor position on mode detection and natural frequencies. Thus, using multiple sensors could be advantageous in accurately detecting all modes and determining the implant loosening state more precisely.

Tumor stiffness is drawing attention as a novel axis that is orthogonal to existing parameters such as pathological examination. We developed a new diagnostic method that focuses on differences in stiffness between tumor and normal tissue. This study comprised a clinical application of laser resonance frequency analysis (L‐RFA) for diagnosing gastric cancer. L‐RFA allows for precise and contactless evaluation of tissue stiffness. We used a laser to create vibrations on a sample's surface that were then measured using a vibrometer. The data were averaged and analyzed to enhance accuracy. In the agarose phantom experiments, a clear linear correlation was observed between the Young's modulus of the phantoms (0.34–0.71 MPa) and the summation of normalized vibration peaks (Score) in the 1950–4050 Hz range (R2 = 0.93145). Higher Young's moduli also resulted in lower vibration peaks at the excitation frequency, signal‐to‐noise (S/N) ratios, and harmonic peaks. We also conducted L‐RFA measurements on gastric cancer specimens from two patients who underwent robot‐assisted distal gastrectomy. Our results revealed significant waveform differences between tumor and normal regions, similar to the findings in agarose phantoms, with tumor regions exhibiting lower vibration peaks at the excitation frequency, S/N ratios, and harmonic peaks. Statistical analysis confirmed significant differences in the score between normal and tumor regions (p = 0.00354). L‐RFA was able to assess tumor stiffness and holds great promise as a noninvasive diagnostic tool for gastric cancer. This study aimed to differentiate the stiffness between tumor and normal stomach tissue using the laser resonance frequency analysis technique, which measures an object's vibratory characteristics with a laser. As a result, there were significant waveform differences between the tumor and normal regions of the resected gastric cancer specimens. This technology is expected to help improve current methods for diagnosing gastric cancer, which can be difficult endoscopically.