Search Results (473)

Induced-junction silicon photodiodes based on SiO₂/SiN_x surface passivation are attractive for high-accuracy radiometry, but their use in the deep ultraviolet is limited by UV-induced degradation of the dielectric stack. In this work, we investigate the degradation of SiO₂/SiN_x-passivated p-type silicon photodiodes under UV irradiation and evaluate strategies for improving stability through shallow implanted junctions and oxide processing. Capacitance–voltage measurements on MIS capacitors and lifetime measurements on symmetrically passivated wafers show that UV exposure causes a rapid reduction in effective dielectric charge and carrier lifetime, followed by saturation at higher dose, consistent with filling of a finite population of electrically active trap states. Induced-junction photodiodes exhibit rapid photocurrent loss at 222 nm and, in some cases, eventual collapse, indicating that the remaining effective dielectric charge is insufficient to sustain the induced junction. To maintain junction functionality after UV exposure, shallow As- and Sb-implanted junctions are employed, resulting in an initial reduction during 222 nm exposure followed by stabilization at around 80–85% of the initial value up to the highest tested dose of 200 J/cm². Further improvement is achieved by stripping and regrowing the implanted screen oxide before SiN_x deposition, yielding nearly unchanged photocurrent after prolonged 222 nm exposure up to ca. 500 J/cm². These results show that UV stability can be substantially improved by reducing device dependence on dielectric-induced inversion and by improving post-implantation interfacial oxide quality. Full article

Accurate tooth segmentation plays a crucial role in computer-aided dental diagnosis and treatment planning, particularly in applications such as tooth detection, lesion localization, orthodontic analysis, and implant surgery. However, panoramic dental X-ray images often suffer from tooth adhesion, low contrast, and blurred boundaries, making precise delineation difficult and potentially compromising downstream clinical analysis. To address these challenges, we propose a boundary-aware segmentation framework, termed Boundary-Aware ResU-Net (BA-ResUNet), which is built upon a ResU-Net backbone and enhanced with Coordinate Attention (CA) and explicit boundary modeling mechanisms. Specifically, CA modules are introduced into the encoder to improve spatial representation and positional awareness. In addition, a Boundary Extraction Module (BEM) is designed to capture boundary priors from shallow and deep features, while a Boundary Injection Module (BIM) progressively incorporates these cues into the decoder through foreground enhancement and background suppression. This design enables the network to better preserve inter-tooth gaps and improve boundary delineation. Experiments on the MICCAI STS-2D dental dataset demonstrate that the proposed method achieves superior performance in terms of Dice and IoU compared with representative existing methods. Ablation and qualitative analyses further show that CA and BEM/BIM play synergistic roles in improving regional overlap and boundary localization, particularly in challenging cases involving adhesion, low contrast, and indistinct contours. These results indicate that the proposed framework provides a reliable and effective solution for panoramic tooth segmentation and has promising potential for computer-aided dental applications. Full article

Background/Objectives: There is no consensus on whether it is possible to preserve implant retention during deep surgical site infections (SSIs), and there is no widely accepted treatment protocol to date for these patients. The aim of this study is to evaluate the efficacy of the debridement, antibiotics, and implant retention (DAIR) protocol in patients who were treated for degenerative thoracolumbar spinal disorder using spinal instrumentation. Methods: This retrospective study describes the 24-month outcomes of deep SSI that developed in 25 of 720 patients (3.5%) who underwent surgery for thoracolumbar degenerative spinal disorders (disc disease, spinal stenosis, and scoliosis) and were treated according to the DAIR protocol. Results: Of these 25 patients, 18 developed early infection (<1 month), 3 developed delayed infection (1–3 months), and 4 developed late-onset deep infection (>3 months). Staphylococcus aureus was isolated in 56% of the patients. The DAIR protocol was successful in 22 (88%) of the patients, while it failed in 3 (12%). Surgical implants were removed in 25% of patients with late-onset SSI, and only 11.1% with early onset and 0% with delayed SSI. All patients who failed DAIR were smokers. A significant association was found between the Charlson Comorbidity Index and the number of surgical interventions (p = 0.022). Conclusions: In this small retrospective cohort, the DAIR protocol appeared to be a feasible treatment option for deep SSI, particularly in early infections. Implant removal may be considered when infection persists after repeat DAIR or when implant loosening is observed. Full article

Background/Objectives: Computer-aided surgical planning (CASP) technologies, including virtual surgical planning (VSP), 3D printed cutting guides, and patient-specific implants, have been increasingly applied to deep circumflex iliac artery (DCIA) free flap reconstruction of maxillofacial defects. Despite growing adoption, no systematic review has specifically evaluated their accuracy and clinical outcomes. This study aimed to comprehensively assess the impact of CASP on reconstruction accuracy, operative efficiency, flap survival, and implant rehabilitation in DCIA flap surgery. Methods: A systematic search of PubMed, Web of Science, and Google Scholar was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Studies reporting CASP-assisted DCIA free flap reconstruction with three or more patients were included. Methodological quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) checklist and the Cochrane Risk of Bias 2.0 tool for the randomized controlled trial (RCT). Results: Thirty studies (1 RCT, 13 comparative, and 16 non-comparative) involving 844 patients were included. VSP with 3D-printed cutting guides was the most frequently used technology (n = 22). Mean linear deviations between planned and actual outcomes ranged from 0.40 to 4.4 mm, with most studies reporting 0.7–2.7 mm. The sole RCT demonstrated significantly better accuracy (1.3 vs. 5.5 mm, p < 0.001) and shorter reconstruction time (16 vs. 39 min, p < 0.001) with CASP. Flap survival ranged from 90% to 100%. Conclusions: CASP technologies, particularly VSP with 3D-printed cutting guides, appear to improve the accuracy and predictability of DCIA flap reconstruction. However, the evidence base is predominantly retrospective and heterogeneous; prospective multicenter studies with standardized outcome measures are needed before definitive clinical guidelines can be established. Full article

Insertion of microelectrode arrays (MEAs) into brain tissue remains a mechanical challenge, especially for long, thin probes designed to access deep structures. This study investigates the mechanical properties of 5 mm long amorphous silicon carbide (a-SiC) probes with different geometries and the effect of vibration-assisted insertion on penetration into rat brain. Methods: Two planar a-SiC probe designs were fabricated with identical lengths and thicknesses but differing width geometries: one with a uniform width (175 µm) and the other with a tapered shape (tapering from 175 to 75 µm). Critical buckling forces (FCs) were estimated by finite element modeling (FEM) and validated experimentally. Insertion mechanics were assessed in a brain mimic of 1.2% agarose gel at varying insertion speeds (20–1000 µm/s) and in vivo by implantation in rat cortex. Insertion metrics included penetration force (F_P), cortical dimpling depth (D_d), maximum insertion force (F_max), and success rate of insertion, all evaluated with and without vibrational assistance. Results: The tapered design exhibited lower penetration force and higher insertion success compared to the uniform-width probe, despite having a lower critical buckling force. An optimal insertion rate of 100 µm/s was identified, balancing insertion time with low F_max and high insertion success across designs. Higher F_P and D_d with a lower success rate were observed for uniform probes compared with tapered probes in rat brain. Vibration-assisted insertion was then investigated with tapered probes. Applying vibration significantly reduced F_P, whereas D_d and F_max remained unchanged. Notably, in 40% of actuated insertions in rat, no detectable F_P peak was observed, suggesting unimpeded pial penetration. Conclusions: A tapered probe geometry and vibration-assisted insertion can reduce F_max and F_P while enhancing the insertion success rate for probe penetration in rat brain. These strategies are generally applicable to long-shank MEA insertions in brain and may inform design and insertion strategies. Full article

Background and Objectives: Although the anterior mandible is generally considered a safe region for implant placement, injury to the medial lingual foramen (MLF) may result in significant vascular complications. Accurate identification of this structure is challenging due to its small size, low volumetric representation, and anatomical variability. This study aimed to evaluate the anatomical characteristics of the MLF using cone-beam computed tomography (CBCT) and to develop and validate a deep learning-based approach for its automated detection and segmentation. Materials and Methods: A total of 106 CBCT scans were retrospectively analyzed to assess the morphology and position of the MLF. Manual pixel-wise annotations of the complete canal trajectory were performed on sagittal slices and used to train convolutional neural network models based on a U-Net-derived framework. Multiple configurations, including multi-class, binary, two-dimensional, and three-dimensional approaches, were evaluated. Given the extremely limited volumetric representation of the MLF, severe class imbalance represented a major challenge during model training and evaluation. Model performance was assessed using the Dice similarity coefficient, precision, recall, and Hausdorff distance. External validation was performed on an independent dataset of 10 CBCT scans. Results: The MLF was identified in all patients, with a single canal observed in 63% of cases. The sagittal-plane binary segmentation model achieved the best performance, with a test Dice score of 0.79, precision of 0.88, and recall of 0.73. External validation demonstrated a Dice score of 0.81, precision of 0.89, and recall of 0.71. The 95th percentile Hausdorff distance was 2.6 mm, and the mean center-point localization error was 1.2 mm. The model correctly detected the MLF in 90% of external cases. Conclusions: Deep learning-based segmentation of the MLF is feasible and may support automated localization assistance during preoperative CBCT assessment. Performance was influenced by the alignment between the annotation strategy and model input, highlighting an important consideration for small-structure segmentation. Further validation on larger multicenter datasets is required before clinical implementation can be considered. Full article

Alumina is a long-used dental and medicinal biomaterial. It is considered one of the best jaw implant materials and has greater antibacterial resistance than titanium (Ti6Al-4V). 3D-printed alumina dental implants were tested in NaCl and Ringer’s solutions for electrochemical corrosion. In six studies, linear polarisation (LPR), electrochemical impedance spectroscopy (EIS), linear sweep voltammetry (LSV), and SEM were used to assess, compare, and elucidate corrosion mechanisms in 3.5% NaCl solution and Ringer’s solution at 25 °C, 45 °C, and 65 °C. At 25–65 °C, alumina in NaCl had corrosion rates of 0.000016–0.000013 mm/yr. Polarisation resistance was good even in a chloride-rich environment at high temperatures, showing effective corrosion protection. The EIS test indicated that the alumina film’s excellent dielectric and insulating capabilities prevented deterioration of the alumina substrate in a concentrated chloride solution. The SEM showed no deep pits. Full article

Maxillofacial defects impair facial aesthetics and oral function, arising from trauma, tumor resection, or congenital anomalies; however, reconstruction using Computer-Aided Design (CAD) and autologous grafts remains complex and time-intensive, and is associated with donor-site morbidity. Although deep learning (DL) has advanced automated reconstruction, existing models often address isolated tasks, lack integrated multi-scale feature learning, and rely on small datasets. This study proposes the Maxillofacial Implant-generation Network (MaxI-Net), a fast, resource-efficient three-dimensional DL framework for end-to-end maxillofacial defect reconstruction and patient-specific implant generation, with a completion step of cavity filling within the assembly. The model employs a 3D encoder–bottleneck-decoder architecture integrating hybrid dilated convolutions, residual connections, squeeze-and-excitation (SE) blocks, and 3D Convolutional Block Attention Modules (CBAM) with multi-scale feature fusion. It was trained on 921 Cone Beam-Computed Tomography (CBCT) scans, augmented to 11,973 maxillary defect pairs, using Dice loss and Adam optimisation with Automatic Mixed Precision, and benchmarked against UNet, UNETR, SegResNet, and SwinUNETR. MaxI-Net achieved the following: superior Dice Similarity Coefficient (DSC) = 0.778; 95th percentile Hausdorff Distance (HD95) = 3.453 mm; DSC Standard Deviation (SD) = 0.094; 95% confidence interval (CI) for mean DSC: 0.775–0.782). It was statistically validated against all competing architectures via pairwise Wilcoxon signed-rank tests, with significant DSC improvements confirmed across all comparators (p < 0.001) and rank-biserial effect sizes ranging from r = 0.250 against the closest competitor SegResNet* with high efficiency (0.06 s/volume; 9.6 min/epoch). Internal cavity filling of the generated implants was performed as a brief manual post-processing step in Autodesk Fusion 360 prior to biomechanical validation. Biomechanical validation using a finite element analysis (FEA) of polyether–ether–ketone (PEEK) implants (~26.53 g) showed 41% stress reduction under physiological loads (100–400 N), predicting a ~9.2-year lifespan. Full article

Background: Total knee arthroplasty in patients with hemophilia remains the most effective surgical intervention for end-stage hemophilic arthropathy, yet it poses unique surgical and perioperative challenges that are rarely encountered in standard osteoarthritis cases. This article synthesizes technical, anatomical, and perioperative considerations specific to hemophilic patients and integrates prospective clinical data derived exclusively from the hemophilic cohort of our long-term study (twenty patients, twenty knees; 2015–2024). Emphasis is placed on deformity correction, bone loss management, implant selection, hemostatic strategies, transfusion patterns, and perioperative pitfalls. The objective is to provide a comprehensive narrative reference for surgeons managing complex hemophilic knees, consolidating both evidence-based recommendations and practical perioperative “tips and tricks” accumulated across more than a decade of clinical experience. Methods: This prospective observational study evaluated twenty consecutive male patients with hemophilia who underwent primary total knee arthroplasty for advanced hemophilic arthropathy between 2015 and 2024 at our institution. The following variables were collected: operative time measured from skin incision to skin closure, postoperative transfusion requirement, length of hospitalization measured in days, early postoperative complications, and functional recovery as assessed by the Knee Society Score. Early complications included postoperative bleeding or hematoma, superficial or deep infection, and stiffness requiring intensive physiotherapy or manipulation under anesthesia. Results: The mean age at the time of surgery was 44.8 years with a standard deviation of 7.2 years, ranging from 31 to 59 years. The mean operative time in the hemophilic cohort was 154.54 min with a standard deviation of 18.36 min. The range of operative time was from 120 to 180 min. Nine of the twenty patients, representing 45 percent, required postoperative blood transfusion. The mean length of hospital stay in the hemophilic cohort was 12.3 days with a standard deviation of 2.38 days, ranging from 9 to 17 days. The mean Knee Society Score improved from 38 points preoperatively to 82 points at final follow-up, representing a mean increase of 44 points. Conclusions: Total knee arthroplasty in hemophilic patients is safe and effective when specialized surgical techniques, comprehensive synovectomy, precise deformity correction, optimized hemostasis, and structured postoperative coagulation factor replacement are implemented. Functional outcomes and prosthetic survival are excellent in experienced centers. Full article

Tooth segmentation from dental meshes is a fundamental step in clinical applications such as computer-aided orthodontics and dental implantation. Compared with mature image segmentation, deep learning-based mesh segmentation research is currently in a high-speed development stage. This study follows a dual-flow personalized feature learning scheme based on meshes and researches high-resolution mesh segmentation problems for clinical needs, proposing a dual-flow deep learning architecture called Position Shape Network (PSNet). Its basic idea includes continuously adjusting the feature map size in the network layer to enhance the model’s generalization ability and designing a reasonable branch structure to personalize the learning of position attributes represented by coordinates and shape attributes represented by surface perimeter area. In addition, it is proposed that the resolution of the validation set should be determined by comprehensively analyzing and simplifying errors to ensure the credibility of the model evaluation. Under this evaluation system, PSNet was compared with relevant authoritative methods in experiments, and the results verified the rationality and efficiency of the method and viewpoint proposed in this paper. Full article

Background: Artificial intelligence (AI) is becoming increasingly integrated into orthopaedic surgery for tasks such as implant positioning, dislocation risk prediction, and surgical decision-making. However, the current evidence varies widely across anatomical regions and applications. Methods: A structured narrative review was conducted using PubMed and Web of Science Core Collection to identify studies applying machine learning or deep learning in orthopaedic procedures, focusing on parameters such as the anatomical region addressed, data types used, primary AI tasks, evaluation designs, and validation strategies. Reviews and meta-analyses were excluded. Study selection was summarized using a PRISMA-style flow diagram, and included studies were narratively synthesized according to anatomical region, AI task, imaging modality, validation strategy, and clinical relevance. Results: We identified three main application areas: (1) AI in imaging-driven planning and implant positioning, often linked with navigation or robotic systems; (2) postoperative evaluation related to implants; and (3) prediction of clinically relevant outcomes such as dislocation risk. The strongest evidence is found in hip arthroplasty, where AI improves measurement accuracy and workflow efficiency, whereas applications in knee, shoulder, and spine surgery are less developed and often supported by smaller studies. Although existing risk prediction models demonstrate good performance, their generalizability is hindered by limited external validation and inconsistent reporting. Conclusions: Overall, while AI shows significant promise in enhancing various aspects of orthopaedic surgery, stronger links between technical advancements and patient outcomes are needed. Future research should prioritize extensive validations, workflow-aware evaluations, failure analysis, and adherence to AI-specific reporting guidelines to facilitate safe and effective clinical implementation. Full article

Objective: Previous studies of deep brain stimulation-related (DBS) artifacts in magnetoencephalography (MEG) have largely focused on the sensor level. In contrast, far less is known about their effects at the source level, where neuroscientific interpretations are typically derived. This study aims to quantify how DBS artifacts distort source-level MEG imaging. Methods: The study used a phantom-based experimental setup to assess dipole-fitting accuracy while systematically varying the stimulation amplitude, DBS electrode configuration, and the distance between the dipole and the DBS electrode. Results: Dipole location, angle, and amplitude errors remained within modest ranges, with the largest location and angle errors occurring at 5 mA ring-electrode stimulation (6.19 mm and 8.31 deg, respectively) and the largest amplitude errors at 15 mA ring electrodes (13.05 nAm). Location and angle errors increased significantly as the dipole moved closer to the DBS electrode, while amplitude error showed no such relationship. Continuous head position indicator coil signal quality remained stable and reliable at DBS on condition, compared to DBS off. Conclusions: The stimulation itself does not significantly impair MEG dipole estimation, as fitting errors are similar with DBS on and off. The study introduces a quantitative framework to systematically assess DBS-related distortion via dipole-fitting error, which can also be extended to evaluate noise from other implanted or external devices. Full article

We investigated whether bone morphology alone can inform implant selection in shoulder arthroplasty using a hypothesis-driven deep learning framework applied to preoperative computed tomography (CT) scans. The proposed approach extends a previously validated segmentation and pathology-staging pipeline by introducing implant-type prediction and a controlled human–AI comparison. The workflow combines CEL-UNet for 3D bone segmentation with ArthroNet⁺, a multi-task network assessing osteophytes, joint-space narrowing, humeroscapular alignment, and implant type. Trained on a multicenter cohort of 600 patients, CEL-UNet achieved Dice scores of 0.99 for the humerus and 0.98 for the scapula. ArthroNet⁺ achieved high performance in pathology classification (up to 95% for alignment tasks). Under morphology-only conditions, ten orthopedic surgeons achieved 61% accuracy with low inter-rater agreement (Fleiss’ $\mathsf{\kappa }\approx 0.15$), while the model reached 78% agreement with the implant choices observed in the dataset, reflecting the ability to reproduce clinical decision patterns rather than to identify an optimal implant selection. This performance is characterized by a class-dependent asymmetry, with higher recall for reverse implants than for anatomical ones. These findings indicate that bone morphology provides a measurable but incomplete signal for implant selection, and should therefore not be interpreted as reflecting clinical decision-making performance. The framework quantifies the morphology-driven component of surgical decision making under controlled conditions, supporting future integration with multimodal clinical data. Full article

Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic sinusitis and bone graft failure after surgery. These risks must be carefully evaluated using preoperative radiographic images. It is time-consuming for physicians to manually perform this process, and details are overlooked due to a lack of clinical experience, which can increase surgical risks. Methods: This study aims to overcome these clinical challenges and improve the reliability of radiographic evaluation. In this study, a hybrid deep learning model is proposed for the automatic detection of the maxillary sinus ostium. The proposed model combines the local feature extraction power of CNN-based models with the global context modeling capabilities of transformer-based models, creating an effective model. Additionally, the gated fusion technique efficiently combines features from various designs, significantly enhancing classification performance. Results: The proposed model was compared with six different ViT and CNN architectures established in the literature. While the highest test accuracy among pre-trained models was 89.36%, the proposed hybrid model achieved 95.03%, demonstrating strong clinical diagnostic performance. Conclusions: Based on the performance metrics obtained, we believe the proposed model can be used to determine the patency of the maxillary sinus ostium. This will lighten the workload for specialists and minimize traditional errors. Full article

Deep brain stimulation (DBS) with implantable pulse generators (IPGs) is widely used in the treatment of movement disorders. Rechargeable IPGs (RC-IPGs) were developed to extend device longevity and address the limitations of battery life in non-rechargeable IPGs. However, data regarding patient compliance and device-related complications remain limited. Therefore, this retrospective observational study evaluated compliance, satisfaction, and complications in patients with RC-IPGs. Compliance in 42 patients with RC-IPGs was evaluated using the Timmermann questionnaire together with additional questions regarding device preference, complaints, and complications. Statistical analyses were performed using NCSS software (Number Cruncher Statistical System, version 2020; NCSS LLC, Kaysville, UT, USA). Although a substantial percentage (42.9%) of patients needed help during recharging, the overall satisfaction score was high (96% of the maximum possible score), and 95.2% of patients preferred RC-IPGs if a repeat DBS would be required, and the rate of RC-IPG complications (7.1%) was low. The patients rated the display screen with the lowest scores (54.05%), mainly those who underwent two or more DBS surgeries. The training subscore showed a statistically significant negative correlation with age (r = −0.531, p = 0.001), and dystonia patients, constituting the youngest group in the cohort, rated training with higher points. This study provides additional data on patient compliance and safety of RC-IPGs. These findings may contribute to a better understanding of patient experience and factors affecting compliance with rechargeable systems. Full article