Search Results (648)

Background/Objectives: Cochlear implants (CIs) constitute a viable method for auditory rehabilitation in patients with profound sensorineural hearing loss after temporal bone fractures (TBFs). These patients comprise a challenging population due to the anatomical deformity and neural injury. Methods: By performing this systematic review, we attempted to evaluate the viability of CIs in the context of TBF. The literature search, across Pubmed/MEDLINE, Scopus and Google Scholar, was performed under the PRISMA guidelines. The selected time period was from December 1995 to September 2025. The final analysis included 11 manuscripts. The majority of the studies were retrospective case series with a moderate risk of bias. Results: The primary outcome was postoperative auditory function, evaluated with speech perception tasks and aided sound-field pure-tone audiometry. The secondary outcomes were the report of radiological and electrophysiologic prognosticators of implants’ viability, timing of surgery, procedural feasibility and complications. Across the studies, CIs conferred meaningful auditory benefit when the cochlear nerve was intact. High-Resolution Computed Tomography (CT) was utilized for TBF classification and cochlear patency, whereas Magnetic Resonance Imaging (MRI) and a promontory test were crucial for the assessment of neural integrity. Prompt placement, optimally within 12 months after trauma, was related to improved outcomes by limiting cochlear fibrosis and ossification. Despite patients’ impedance fluctuation, restricted speech perception in noise and frequent abnormal facial nerve excitation, the overall audiologic and speech discrimination results are comparable to non-trauma recipients. Conclusions: A CI appears to be the choice of treatment over auditory brainstem implants, as long as the cochlear nerve remains intact. Rapid implantation in well-selected patients coupled with ordinal mapping and follow-up can restore dysfunctional hearing and improve patients’ quality of life. Full article

Background/Objectives: Successful cementless total knee arthroplasty (TKA) requires adequate bone quality. However, reliable tools for intraoperative assessment remain limited. This study aimed to introduce a novel visual grading system for evaluating femoral bone during surgery and to assess its correlation with actual bone mechanical properties and suitability for cementless fixation. Methods: We prospectively recruited 193 patients receiving posterior-stabilized TKA. Intraoperatively, femoral cutting surfaces were classified into four visual grades (Excellent, Good, Fair, Poor) considering pore appearance and contour integrity. Femoral bone specimens were harvested during box preparation, and bone mechanical properties were measured through indentation testing. Spearman correlation was used to evaluate the relationship between visual grades and bone mechanical properties. Fisher’s exact test was used to evaluate the distribution pattern of cementless suitable and cemented mandatory classifications across visual grading. Receiver operating characteristic (ROC) analysis was used to evaluate diagnostic accuracy for each visual grade cutoff. Results: Visual grade strongly correlated with bone mechanical properties (Spearman’s ρ = 0.881, p < 0.01). Cementless suitable cases were predominantly distributed in Good/Excellent visual grades, while cemented mandatory cases were mostly found in Fair/Poor grades. However, 8% of Good visual grade specimens exhibited strength warranting cemented fixation, and 18% of Fair visual grade specimens demonstrated adequate mechanical properties for cementless fixation. Using the Good visual grade as a cutoff threshold, ROC analysis showed excellent diagnostic accuracy (AUC = 0.941) with high sensitivity (89%) and specificity (94%). Conclusions: The authors’ novel intraoperative visual assessment system demonstrated significant correspondence to measured bone mechanical properties in the distal femur and showed high accuracy in determining suitability for cementless TKA in Asian individuals. Given the ethnic homogeneity of this cohort, further validation in diverse populations is required to generalize these findings. Full article

Artificial intelligence models, which have begun to be used in every field of science in recent years, have also started to come to the forefront in the classification of avians using bones. This study aimed to identify breeds of domestic fowl (Gallus gallus domesticus L. 1758) using morphometric measurements obtained from the tarsometatarsus bone and machine learning. A total of 328 tarsometatarsus specimens from two different modern domestic fowl breeds were used. A model was developed by performing 10 different morphometric measurements on each tarsometatarsus, and 3280 data points were obtained. Before model development, data cleaning and necessary assessments were carried out, and gaps were identified. In pre-processing and data partitioning, 70% of the data was used for training, and 30% was reserved for testing the developed model. To determine the differences between breeds, evaluations were performed using classical supervised learning algorithms in machine learning. Random forest (RF), support vector machine with radial kernel (SVM-RBF), and the generalized linear model (GLM, logistic regression) were used for model development, while model validation was performed using cross-validation (CV) metrics. After model validation, variable importance, feature selection, correlation analysis, dimensionality reduction, and multicollinearity were performed. The developed model, using morphological measurements obtained from the tarsometatarsus, distinguishes between breeds with high accuracy. The discriminative signal is extremely strong, allowing multiple modeling strategies (tree-based, kernel-based, and linear) to perfectly distinguish between the two breeds. Among the morphometric measurements, Ac (extension of the trochlea metatarsi IV) and Bmit (breadth of the middle trochlea) were found to be the strongest distinguishing features. This developed model combines morphometric data and artificial intelligence to offer an innovative method for scaling, accelerating, or improving applications in science. By expanding the model’s database with measurements obtained from the tarsometatarsus bones of different breeds, it was demonstrated that breed differences can be quickly and accurately determined using a minimal number of measurements from tarsometatarsus bones. Full article

Objective: We aimed to evaluate the morphometric characteristics of the internal acoustic meatus (IAM) using high-resolution computed tomography (CT), with emphasis on sex- and age-related differences, with particular emphasis on the IAM orientation angle as a less-studied spatial parameter and its potential clinical and forensic relevance. Methods: Temporal bone CT scans of 162 patients (94 females, 68 males; age 1–77 years) were retrospectively analyzed. Measurements included the IAM inlet diameter, length, mid-diameter, lateral angle (LA), and orientation angle. Inter-observer agreement was assessed in 30 randomly selected cases. Morphometric parameters were compared by sex and age using t-tests and Mann–Whitney U tests. Results: Mean IAM lengths were 11.0 mm (right) and 11.1 mm (left), and the mean mid-diameter was 4.2 mm bilaterally. IAM lengths and diameters showed no significant sex- or age-related differences (p > 0.05). In contrast, LA and orientation angle differed significantly by sex (p < 0.05), with females showing higher LA values, which may influence posterior fossa surgical exposure. Conclusions: IAM size parameters are largely independent of sex and age, whereas lateral and orientation angles exhibit sex-related variation. Preoperative evaluation of IAM orientation on CT can support skull base surgical planning, and LA may provide supportive morphometric information in forensic contexts, although it should not be considered a standalone sex classification parameter. Full article

Background: Gingivitis and periodontitis are progressive inflammatory diseases affecting the tissues surrounding the teeth; gingivitis involves reversible gingival inflammation, whereas periodontitis is a more advanced condition characterized by irreversible tissue destruction, including clinical attachment and alveolar bone loss. Salusin-α and salusin-β are inflammation-related polypeptides that may reflect periodontal inflammatory burden; however, salivary data in periodontal diseases are lacking. This study aimed to evaluate the salivary salusin-α and salusin-β levels in individuals with gingivitis and periodontitis. Methods: Saliva samples were collected from a total of 80 systemically healthy non-smoker individuals classified into three groups: gingivitis (n = 27), stage III grade B periodontitis (n = 27), and healthy participant (n = 26) based on the 2017 Periodontal Classification criteria. Salusin-α and salusin-β levels in saliva were quantified using enzyme-linked immunosorbent assays (ELISA). Statistical analysis utilized one-way ANOVA, Student’s t-test, and Receiver Operating Characteristic (ROC) curve analysis. Results: Compared to the healthy group, salivary levels of salusin-α and salusin-β were found to be significantly elevated in periodontitis groups (p < 0.05), not gingivitis (p > 0.05); moreover, the increase in both markers was significantly greater in the periodontitis group than in the gingivitis group (p < 0.05). Conclusions: Our finding suggests that salusins play a role in the inflammatory processes of periodontal diseases. The increase in salusin-α and salusin-β levels in the periodontitis suggests that these parameters may serve as biomarkers. Full article

Background/Objectives: Impacted third molars are frequent and may increase surgical complexity, particularly when the mandibular third molar is in close proximity to the inferior alveolar canal (IAC). This study aimed to estimate the prevalence and impaction patterns of third molars in a Portuguese population and to characterize, using a nested CBCT subsample, the three-dimensional relationship between mandibular third molars and the IAC, including cortical integrity and lingual plate thickness. Methods: A retrospective observational analysis of 1062 orthopantomograms (OPGs) was performed to determine the prevalence and panoramic patterns using Winter, Pell and Gregory classifications and Rood–Shehab signs. A consecutive CBCT subsample (n = 205) was assessed for IAC position, contact status (no contact; contact with cortical bone; contact without cortical bone), cortical integrity, and lingual plate thickness. Descriptive statistics were complemented by effect sizes to support clinical interpretability. Results: The prevalence of impacted third molars was 34.9%, occurring predominantly in the mandible. Vertical angulation was the most prevalent pattern in both jaws. In the CBCT subsample, IAC position and contact patterns varied widely, and loss of cortical integrity was more often observed when panoramic high-risk signs were present. No clinically meaningful left–right asymmetry was identified across key anatomical risk indicators. Conclusions: In this Portuguese cohort, impacted third molars showed consistent panoramic patterns, while CBCT provided clinically relevant three-dimensional risk descriptors—particularly IAC contact type and cortical integrity—supporting selective CBCT use based on anatomical risk indicators rather than routine imaging. Full article

Cerebral palsy (CP) is a developmental disability caused by injury to the fetal or infant brain, affecting between 1.6 to 3.7 per 1000 live births worldwide. Ambulatory patients with cerebral palsy experience various gait problems, for which they seek treatment from medical professionals. Varus foot deformities are among the most problematic for patients. Varus foot deformity is characterized by the inner border of the foot being tilted upward and the hindfoot inward, increasing weightbearing on the lateral aspect of the foot. This positioning increases weight-bearing pressure under the lateral (outside) of the foot and often under the fifth metatarsal head when walking. As such, varus foot deformity can contribute to in-toeing, make shoe and brace-wearing difficult and painful, compromise gait stability, and sometimes lead to metatarsal fractures. Current knowledge of CP etiology and classifications, as well as principles and advances in assessment and treatment decision making for varus foot deformities, are outlined in this narrative review. In younger children with flexible deformities, non-operative interventions such as bracing, botulinum toxin injection, and serial casting are effective. The literature and expert consensus suggest that, if possible, surgery should be delayed until after the age of 8 years. When surgery is indicated, soft tissue procedures are used for flexible deformities. In addition to the soft tissue procedures, bone surgery is needed for rigid deformities. Careful pre-operative foot assessment is needed, including assessment of deformity flexibility and range of motion, X-rays, and computerized gait analysis if possible. Strategies are presented for thorough assessment when gait analysis is not available or feasible. Research reports of surgical outcomes for soft tissue and bony correction are positive, but should be interpreted with caution. The quality of evidence on surgical outcomes is compromised by use of varying research design methods and selection of outcome measures, with few including measures of function or patient-reported outcomes. It is recommended that surgical outcome be assessed using standardized assessment tools, such as the Foot Posture Index, which have had their validity and reliability established. Recent advances in 3D kinematic foot model development and musculoskeletal modeling have the potential to greatly improve surgical outcomes for patients with CP. Full article

Background: Osteoporosis (OP) is characterized by reduced bone mineral density and increased fracture risk. Many spinal surgery patients have undiagnosed OP due to the lack of preoperative screening, leading to postoperative complications. Magnetic resonance imaging (MRI), a routine, non-invasive tool for spinal assessment, offers potential for opportunistic OP detection. This study aimed to develop deep learning models to identify OP using lumbar MRI. Methods: We retrospectively enrolled 218 patients (≥50 years) who underwent both lumbar MRI and dual-energy X-ray absorptiometry (DXA). After segmentation of vertebral bodies from T1- and T2-weighted MRI images, 738 images per sequence were extracted. Separate convolutional neural network (CNN) models were trained for each sequence. Model performance was evaluated using receiver operating characteristic curves and area under the curve (AUC). Results: Among tested classifiers, EfficientNet b4 showed the best performance. For the T1-weighted model, it achieved an AUC of 82%, with a sensitivity of 85% and specificity of 79%. For the T2-weighted model, the AUC was 83%, with a sensitivity of 86% and specificity of 80%. These results were superior to those of InceptionResNet v2 and ResNet-50 for both sequences. Conclusions: The AI models provided reliable OP classification without additional imaging or radiation. AI-based analysis of standard lumbar MRI sequences can accurately identify OP. These models may assist in early detection of undiagnosed OP in surgical candidates, enabling timely treatment and perioperative strategies to improve outcomes and reduce healthcare burden. Full article

Background/Objectives: Accurate classification of primary bone tumors from radiographic images is essential for early diagnosis, appropriate treatment planning, and informed clinical decision-making. While deep convolutional neural networks (CNNs) have shown strong performance in medical image analysis, their high computational complexity often limits real-world clinical deployment. This study aims to develop a lightweight yet highly accurate model for multi-class bone tumor classification. Methods: We propose Bone-CNN, a computationally efficient CNN architecture specifically designed for radiograph-based classification of primary bone tumors. The model was evaluated using the publicly available Figshare Radiograph Dataset of Primary Bone Tumors, which includes nine distinct tumor classes ranging from benign to malignant lesions and originates from multiple imaging centres. Performance was assessed through extensive experiments and compared against established baseline models, including DenseNet121, EfficientNet-B0, and MobileNetV2. Results: Bone-CNN achieved a test accuracy of 96.52% and a macro-AUC of 0.9989, outperforming all baseline architectures. Both quantitative and qualitative evaluations, including confusion matrices and ROC curve analyses, demonstrated robust and reliable discrimination between challenging tumor subtypes. Conclusions: The results indicate that Bone-CNN offers an excellent balance between accuracy and computational efficiency. Its strong performance and lightweight design highlight its suitability for clinical deployment, supporting effective and scalable radiograph-based assessment of primary bone tumors. Full article

Background: Altered passive eruption (APE) is one of the etiological factors associated with excessive gingival display and is commonly treated with esthetic crown lengthening (ECL). However, existing classification systems provide limited guidance for selecting appropriate treatment approaches. Objectives: The aim of this study was to evaluate (1) the expected outcome of ECL in eliminating unattractive excessive gingival display (4 mm) based on digital smile assessment and (2) the distribution of teeth and patients according to the modified APE classification. Methods: Forty-two Thai patients with APE underwent clinical examination, digital smile assessment, intraoral scanning, and CBCT. Predicted gingival display (PGD) was calculated to assess the expected outcomes of ECL. The modified APE classification, incorporating CEJ–BC distance and buccal bone thickness, was analyzed at both the tooth and patient levels. Results: A total of 252 maxillary anterior teeth were assessed. Most patients (78.57%) presented with APE and hyperactive upper lip. The mean gingival display (GD) was 6.04 ± 1.76 mm, with GD ≥ 4 mm observed in 92.86% of patients. The mean PGD was 3.56 ± 1.71 mm, and ECL was predicted to reduce GD to < 4 mm in 66.67% of patients. Teeth were classified as Class I (28.97%), II (15.48%), III (41.27%), and IV (14.28%); only Types II (11.9%) and III (88.1%) occurred at the patient level. Conclusions: ECL performed at the CEJ level is predicted to eliminate excessive gingival display in approximately two-thirds of APE patients. The modified APE classification offers guidance for selecting surgical approaches, highlighting the necessity of open-flap procedures and the limited applicability of flapless approaches. Full article

Recent advances in biomaterials, immunomodulation, stem cell therapy, and biofabrication are reshaping maxillofacial surgery, shifting reconstruction paradigms toward biologically integrated and patient-specific tissue regeneration. This review provides a comprehensive synthesis of current and emerging strategies for bone and soft-tissue regeneration in the craniofacial region, with particular emphasis on bioactive ceramics, biodegradable polymers, hybrid composites, and stimuli-responsive smart materials. We further examine translational technologies such as extracellular vesicles, decellularized extracellular matrices, organoids, and 3D bioprinting, highlighting key challenges such as bioink standardization, perfusion limitations, and regulatory classification. Maxillofacial surgery is positioned for a paradigm shift toward personalized, biologically active, and clinically scalable regenerative solutions. Full article

Objectives: This study aims to evaluate the efficacy of hydroxyapatite-tricalcium phosphate (HAP-TCP) as a bone substitute in subantral augmentation for dental implants. Specifically, it investigates the effects of HAP-TCP on bone quality, density, and integration with implants over time. Methods: A prospective controlled longitudinal study was conducted on 22 patients (39–75 years of age) undergoing subantral augmentation and dental implantation. A total of 52 sites of augmented bone and 67 sites of native bone were analyzed using computed tomography (CT) to assess bone density in Hounsfield Units (HU), insertion torque measurements, and the Misch classification for bone quality. Augmented and native bone measurements were compared within each patient. Results: The augmented bone exhibited an average density of 1132.6 ± 334.9 HU, which is significantly higher (45.9%) than the average density of native bone at 519.3 ± 395.0 HU. Insertion torque values in the HAP-TCP augmented sites averaged 35 N·cm, showing a 71.4% increase compared to adjacent native bone sites (25 N·cm). The study found notable improvements in bone homogeneity and vascularization within the augmented zones. Conclusion: HAP-TCP demonstrates significant potential as a reliable and effective synthetic bone substitute for subantral augmentation in dental implants. It yields higher radiodensity and insertion torque than adjacent native bone, while mitigating complications associated with autogenous grafts. These observational findings support the potential clinical use of HAP-TCP for sinus augmentation. Full article

Background: The global increase in orthopedic implant use—both for trauma fixation and arthroplasty—has profoundly transformed musculoskeletal surgery. As a consequence, fractures occurring in the presence of implants have become more frequent and clinically relevant. Yet, these injuries are currently described using highly heterogeneous terminology, including periprosthetic (fracture occurring in the presence of a prosthetic joint replacement) peri-implant (fracture occurring around an osteosynthesis or fixation device), implant-related, and hardware-related fractures (umbrella terms encompassing both prosthetic and fixation devices, used descriptively rather than classificatorily). This coexistence of multiple, context-specific terminologies hinders clinical communication, complicates registry documentation, and limits research comparability across orthopedic subspecialties. Because fractures occurring in the presence of orthopedic implants significantly alter load transfer, muscle force distribution, and musculoskeletal biomechanics, a clear and unified terminology is also relevant for muscle-focused research addressing implant–tissue interaction and functional recovery. Objective: This systematic review aimed to critically analyze the terminology used to describe fractures influenced by orthopedic implants, quantify the heterogeneity of current usage across anatomical regions and publication periods, and explore the rationale for adopting a unified umbrella term—“artificial fracture.” Methods: A systematic search was performed in PubMed, Scopus, and Web of Science from January 2000 to December 2024, following PRISMA guidelines. Eligible studies included clinical investigations, reviews, registry analyses, and consensus statements explicitly employing or discussing terminology related to implant-associated fractures. Data were extracted on publication characteristics, anatomical site, terminology employed, and classification systems used. Quantitative bibliometric and qualitative thematic analyses were conducted to assess frequency patterns and conceptual trends. Results: Of 1142 records identified, 184 studies met the inclusion criteria. The most frequent descriptor in the literature was periprosthetic fracture (68%), reflecting its predominance in arthroplasty-focused studies, whereas broader and more practical terms such as implant-related and peri-implant fracture were more commonly used in musculoskeletal and fixation-related research. Terminological preferences varied according to anatomical site and implant type, and no universally accepted, cross-anatomical terminology was identified despite multiple consensus efforts. Discussion and Conclusions: The findings highlight persistent heterogeneity in terminology describing fractures influenced by orthopedic implants. A transversal, descriptive framework may facilitate communication across subspecialties and support registry-level harmonization. Beyond orthopedic traumatology, this approach may also benefit muscle and musculoskeletal research by enabling more consistent interpretation of data related to muscle–bone–implant interactions, rehabilitation strategies, and biomechanical adaptation. Full article

Shape-memory polymers (SMPs) are a class of smart materials capable of recovering their original shape from a programmed temporary shape in response to external stimuli such as heat, light, or magnetic fields. SMPs have attracted significant interest for biomedical devices and soft robotics due to their large recoverable strains, programmable mechanical and thermal properties, tunable activation temperatures, responsiveness to various stimuli, low density, and ease of processing via additive manufacturing techniques, as well as demonstrated biocompatibility and potential bioresorbability. This review summarises recent progress in the fundamentals, classification, activation mechanisms, and fabrication strategies of SMPs, focusing particularly on design principles that influence performance relevant to specific applications. Both thermally and non-thermally activated SMP systems are discussed, alongside methods for controlling activation temperatures, including plasticisation, copolymerisation, and modulation of cross-linking density. The use of functional nanofillers to enhance thermal and electrical conductivity, mechanical strength, and actuation efficiency is also considered. Current manufacturing techniques are critically evaluated in terms of resolution, material compatibility, scalability, and integration potential. Biodegradable SMPs are highlighted, with discussion of degradation behaviour, biocompatibility, and demonstrations in devices such as haemostatic foams, embolic implants, and bone scaffolds. However, despite their promising potential, the widespread application of SMPs faces several challenges, including non-uniform activation, the need to balance mechanical strength with shape recovery, and limited standardisation. Addressing these issues is critical for advancing SMPs from laboratory research to clinical and industrial applications. Full article

Background: Osteoporosis and osteopenia are prevalent bone diseases characterized by reduced bone mineral density (BMD) and an increased risk of fractures, particularly in postmenopausal women. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, it has limitations regarding accessibility, cost, and predictive capacity for fracture risk. Machine learning (ML) approaches offer an opportunity to develop automated and more accurate diagnostic models by incorporating both BMD values and clinical variables. Method: This study retrospectively analyzed BMD data from 142 postmenopausal women, classified into 3 diagnostic groups: normal, osteopenia, and osteoporosis. Various supervised ML algorithms—including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN)—were applied. Feature selection techniques such as ANOVA, CHI2, MRMR, and Kruskal–Wallis were used to enhance model performance, reduce dimensionality, and improve interpretability. Model performance was evaluated using 10-fold cross-validation based on accuracy, true positive rate (TPR), false negative rate (FNR), and AUC values. Results: Among all models and feature selection combinations, SVM with ANOVA-selected features achieved the highest classification accuracy (94.30%) and 100% TPR for the normal class. Feature sets based on traditional diagnostic regions (L1–L4, femoral neck, total femur) also showed high accuracy (up to 90.70%) but were generally outperformed by statistically selected features. CHI2 and MRMR methods also yielded robust results, particularly when paired with SVM and k-NN classifiers. The results highlight the effectiveness of combining statistical feature selection with ML to enhance diagnostic precision for osteoporosis and osteopenia. Conclusions: Machine learning algorithms, when integrated with data-driven feature selection strategies, provide a promising framework for automated classification of osteoporosis and osteopenia based on BMD data. ANOVA emerged as the most effective feature selection method, yielding superior accuracy across all classifiers. These findings support the integration of ML-based decision support tools into clinical workflows to facilitate early diagnosis and personalized treatment planning. Future studies should explore more diverse and larger datasets, incorporating genetic, lifestyle, and hormonal factors for further model enhancement. Full article