Bioengineering

Multimodal retinal image registration remains challenging due to nonlinear intensity discrepancies and insufficient feature representation across modalities. To address these limitations, we propose VesselRegNet, a joint retinal vessel segmentation and registration framework based on multi-scale feature fusion. The model integrates a multi-scale semantic feature extraction module with a shared prediction head and rotation-equivariant constraints to enhance feature consistency. Unlike conventional approaches relying on binary segmentation masks, the proposed method utilizes dense semantic features to directly guide deformable registration. Experimental results demonstrate that VesselRegNet achieves an average Dice coefficient of 0.664 and SSIM of 0.645, achieving a relative improvement of 4.1% in Dice and 2.5% in SSIM over the strongest baseline (RetinaRegNet), and up to 11.9% improvement over general unsupervised registration frameworks. The proposed multi-scale fusion and joint learning strategy improves registration accuracy under complex retinal deformations. Full article

Surgical sutures are temporary load-bearing biomaterials whose performance in the oral cavity may be altered by postoperative mouthrinses. Seven USP 4-0 suture materials were immersed in four commercial mouthrinses for 3, 7, 10, and 14 days in a balanced factorial design (n = 3 per condition), with artificial saliva as a time-matched reference. Maximum breaking force (Fmax), apparent wet-state mass change, and solution pH were evaluated, and Day 14 re-dried mass was determined in an additional cohort (n = 5). Fmax was significantly affected by material, mouthrinse, time, and all interaction terms (p < 0.001), with material showing the largest main effect (partial η² = 0.957). Mean Fmax decreased by 57.1% for PGA and 53.3% for PGLA between Days 3 and 14 when averaged across mouthrinses. PGLA also declined in artificial saliva (−49.1%), showing that temporal loss was not exclusive to mouthrinse exposure. Mean apparent wet-state mass change ranged from 85.1% for silk to 8.5% for nylon 66 and showed a weak pooled association with Fmax (r = −0.163, p = 0.086); suture-related pH shifts were small. Under static immersion, response was specific to the material–formulation pair and evolved with time. Continuous immersion without medium renewal or prior salivary conditioning does not reproduce intermittent clinical mouthrinse use. Full article

Accurate detection of musculoskeletal abnormalities from radiographs remains challenging because abnormal findings can be subtle and radiographic appearance may vary considerably across examinations. This study aimed to develop and systematically evaluate a deep learning framework incorporating Grad-CAM-based visual attribution for automated musculoskeletal abnormality detection using the publicly available Musculoskeletal Radiographs (MURA) dataset. Four transfer learning architectures, namely DenseNet201, EfficientNetV2-S, ConvNeXt Tiny, and Swin Transformer Tiny, were evaluated for musculoskeletal abnormality classification. The framework incorporated Grad-CAM-based attribution and activation-based weak localization to highlight image regions associated with model predictions without requiring bounding-box annotations during training and a sensitivity-oriented decision threshold to prioritize the detection of abnormal examinations. Model performance was assessed using recall, precision, F1-score, area under the receiver operating characteristic curve (AUC), and confusion matrix analysis. A targeted decision-threshold ablation analysis compared the sensitivity-focused threshold of 0.40 with the conventional 0.50 baseline using the same validation-set probabilities. Previously published results were reviewed to provide contextual information rather than establish comparative superiority. EfficientNetV2-S achieved the highest precision (0.925), F1-score (0.892), and AUC (0.956) on the internal validation set among the evaluated architectures. DenseNet201 achieved the highest recall of 0.876. Previously published performance metrics were reviewed to contextualize the results of the present study. However, differences in datasets, anatomical coverage, preprocessing procedures, and evaluation protocols preclude direct conclusions regarding comparative superiority. At the sensitivity-focused threshold of 0.40, EfficientNetV2-S obtained an accuracy of 0.896 and an F1-score of 0.892 on the internal validation set. Lowering the threshold to 0.40 improved sensitivity from 0.754 to 0.861 and decreased false-negative predictions from 1092 to 618 when compared to the traditional threshold of 0.50. Alongside this improvement, false-positive predictions increased from 150 to 307 and specificity decreased from 0.966 to 0.931. Grad-CAM visualizations and activation-derived bounding boxes provided qualitative information about spatial attribution. However, their correspondence with pathological findings was not independently validated using expert annotations or quantitative localization metrics. On the independent held-out test set, EfficientNetV2-S achieved a recall of 0.826, precision of 0.886, F1-score of 0.855, and AUC of 0.927. The independent evaluation demonstrated lower performance than the internal validation results, highlighting the importance of separating model optimization from final performance assessment. The findings demonstrate the feasibility of integrating transfer learning, Grad-CAM-based visual attribution, activation-based weak localization, and sensitivity-oriented decision-making within a unified framework for musculoskeletal abnormality classification. Although the framework demonstrated promising classification performance, the visual attribution results remain exploratory and do not establish pathological localization accuracy or clinical interpretability. Further evaluation using external clinical datasets, expert-annotated pathological regions, and radiologist assessment is required to establish its generalizability, explanation validity, and potential clinical utility. Full article

Bone stress injuries (BSIs) are overuse injuries to bone that account for substantial time lost for athletes who are training and competing. Each injury varies in healing time based on factors including the Female and Male Athlete Triad (Triad), anatomical location, injury severity, and chronicity. While focused extracorporeal shockwave therapy (ESWT) has been evaluated for management of BSIs, extracorporeal magnetotransduction therapy (EMTT) has been proposed to have synergistic effects. We evaluated return to running in athletes with BSIs treated with combined ESWT and EMTT. Retrospective chart review identified 46 athletes with 50 BSIs (29 female, 17 male; mean age 30.5 ± 13.2 years; body mass index 22.0 ± 2.8 kg/m²). Each athlete was comprehensively evaluated and provided individualized management of all risk factors identified, including Triad, anatomical location, MRI grade, and chronicity (acute < 3 months; delayed/nonunion ≥ 3 months). Each athlete elected to receive weekly to biweekly focused electromagnetic ESWT and EMTT (mean values: 4.8 sessions; ESWT, 3424 pulses/session, mean energy flux density 0.41 mJ/mm²; EMTT: 80 mT, 8 Hz, 7826 pulses/session). Additional ESWT/EMTT were based on factors including persistent pain during return-to-run progression. Most BSIs were high grade ($n=35$); Triad risk was moderate in 21 athletes and high in 6. Forty athletes (87%) returned to pain-free running. Return to run was defined as the start of a patient-reported pain-free return-to-run progression. Among 39 athletes with documented dates, this occurred sooner for acute than delayed/nonunion BSIs (12.9 ± 5.8 vs. 23.8 ± 12.1 weeks, $p=0.002$). Differences by injury grade ($p=0.18$), Triad risk ($p=0.97$), and anatomical classification ($p=0.26$) were not statistically significant, although subgroup sizes were small. While we recognize limitations related to study design and the population studied, our findings suggest the addition of ESWT/EMTT combined with comprehensive management of athletes with BSI may allow for a high rate of return to running. Full article

Artificial intelligence (AI) is increasingly used across the diagnostic pathway, from screening and disease detection to differential diagnosis, prognostic stratification, and treatment-response assessment. This narrative review examines predictive, generative, multi-modal, and agentic AI through a diagnostic-science lens and evaluates the evidence required for translation into clinical practice. Predictive models can identify complex patterns in images, physiological signals, laboratory data, and electronic health records, but high retrospective accuracy does not establish diagnostic utility. Clinical validity and utility depend on the intended use, target population, disease spectrum and prevalence, reference standard, operating threshold, calibration, and the consequences of false-positive and false-negative results. Generative AI may support problem representation, differential diagnosis, information synthesis, and documentation, yet fluent outputs can omit critical alternatives, amplify false premises, or convey unjustified certainty. Multimodal systems may better reflect clinical reasoning by integrating imaging, text, signals, pathology, and molecular data, but they must demonstrate incremental value over the best single-modality test and remain robust to missing data. Agentic systems can coordinate evidence retrieval, test selection, and sequential workflows, thereby increasing the need for bounded permissions, auditability, error recovery, and human escalation. Across all paradigms, a credible pathway to diagnostic implementation requires independent external and prospective validation, representative consecutive patients, subgroup analysis, workflow and human–AI evaluation, clinical impact studies, and post-deployment surveillance. The field should therefore be judged not by model capability alone, but by whether AI measurably improves diagnostic yield, timeliness, safety, equity, and patient-relevant outcomes in real care settings. Full article

Atrial fibrillation (AFib) is the most prevalent sustained cardiac arrhythmia, yet most deep learning detectors are validated on a single cohort and few report uncertainty. We developed a compact 14M-parameter convolutional neural network for binary AFib classification from 10 s, 12-lead ECG at 500 Hz, trained exclusively on the HDXML dataset (67,432 ECGs; 9628 AFib) with on-the-fly augmentation for noise, drift and missing channels. Using one unchanged checkpoint, we evaluated six independently sourced public cohorts totaling over 1.25 million recordings, including MIMIC-IV critical care and two ambulatory Holter cohorts. All metrics are reported with 95% confidence intervals from a cluster bootstrap at the patient, recording or subject levels. ROC–AUC exceeded 0.95 in every cohort, from 0.960 (95% CI 0.956–0.963) on CODE-15 to 0.999 (0.998–0.999) on SPH, including 0.966 (0.965–0.967) on MIMIC-IV with 75.3% sensitivity and 97.7% specificity, 0.963 (0.929–0.987) on CPSC 2021 and 0.995 (0.988–0.999) on MIT-BIH. Performance was stable under progressive lead masking without retraining, and median inference took 110 ms per segment on a server-class CPU without a GPU. A compact network trained on one curated source can therefore generalize across hospitals, countries and recording hardware. This study is retrospective; prospective validation is required before clinical use. Full article

The COVID-19 pandemic exposed persistent weaknesses in health-data interoperability, cybersecurity, provenance, cross-organizational governance, and continuity of digital care. This short communication presents an updated focused narrative synthesis and a five-layer governance framework for integrating blockchain and the Internet of Medical Things (IoMT) in patient-centered digital health. The literature was updated through 10 August 2026, with recent peer-reviewed evidence complemented by authoritative standards and European regulatory sources. The synthesis positions blockchain primarily as a distributed governance, provenance, and audit layer rather than a clinical database or stand-alone security solution, while IoMT provides heterogeneous sensing infrastructure for continuous and distributed care. The framework separates: (1) device and data acquisition; (2) network and edge orchestration, including 5G/SDN where clinically justified; (3) blockchain-based governance; (4) privacy-enhancing data, analytics, and AI; and (5) application and service delivery. Homomorphic encryption, zero-knowledge proofs, secure aggregation, differential privacy, and federated learning are treated as complementary controls with distinct threat models and computational trade-offs. The revised framework also incorporates the European Health Data Space, the EU AI Act and its medical-device interplay, and NIS2 as a regulatory overlay. Across six use-case domains, evidence is strongest for technical feasibility, provenance, and programmable access control; evidence for clinical effectiveness, cost-effectiveness, equity, sustainability, and large-scale deployment remains limited. The proposed model therefore emphasizes standards-based interoperability, minimization of on-chain personal data, endpoint trust, auditable human oversight, and prospective real-world validation. Full article

Soft robotic systems have attracted increasing attention for hand rehabilitation because of their compliance, adaptability, and potential for comfortable human–robot interaction. However, the clinical translation of soft hand rehabilitation robots remains limited by insufficient integration of interactive training, user feedback, and practical wearability. This study presents a multimodal feedback-enhanced soft hand rehabilitation robot based on lattice-structured soft pneumatic actuators (LSPAs), together with a preliminary clinical evaluation. The system integrates a fabric-based soft glove, pneumatic actuation, fingertip force sensing, hand-motion tracking, and interactive virtual rehabilitation tasks to provide coordinated visual, auditory, force-related, and mirror-visual feedback. Material characterization was first conducted to compare three TPU materials with different mechanical properties, followed by system-level evaluation of usability, wearability, and actuator durability. A four-week robot-assisted rehabilitation intervention was then conducted in nine stroke survivors. Participants completed five one-hour sessions per week, including passive training and multimodal interactive training. Clinical outcomes were evaluated using the Brunnstrom motor recovery stage, Fugl–Meyer Assessment of the upper extremity (FMA-UE), National Institutes of Health Stroke Scale (NIHSS), and Activities of Daily Living (ADL) scale, while usability was assessed using the System Usability Scale (SUS). After four weeks, the mean Brunnstrom stage increased from $2.44\pm 2.07$ to $3.67\pm 2.12$, FMA-UE increased from $18.33\pm 25.63$ to $32.67\pm 23.11$, NIHSS decreased from $5.67\pm 4.36$ to $3.22\pm 2.99$, and ADL increased from $49.78\pm 39.53$ to $66.67\pm 22.50$. The mean SUS score was 74.4. TPU420D provided the best durability among the tested configurations, with more than 30 h of recorded use without failure during the experimental period. These findings support the feasibility and usability of the proposed system and provide preliminary evidence for its application in stroke hand rehabilitation. Because of the small sample size, heterogeneous post-stroke status, short intervention period, and absence of a control group, the clinical findings should be interpreted as preliminary rather than confirmatory evidence of therapeutic efficacy. Full article

Aspirin prevents preterm birth in some women with preeclampsia but not others, yet no longitudinal multi-indicator study has examined subtype differences in aspirin response. We aimed to identify subtypes most likely to benefit. In this retrospective cohort of 53,362 deliveries (4505 preeclamptic women), we applied a two-stage machine learning framework using 15 indicators (14 laboratory tests plus systolic blood pressure) from two-time windows: before 16 weeks and within 2 weeks before delivery. A long short-term memory autoencoder with K-means clustering identified subtypes, and causal forest estimated the average treatment effect of aspirin on preterm birth (<37 weeks) for each subtype, adjusted for confounders. Five stable subtypes were identified (silhouette coefficient 0.542; mean adjusted Rand index 0.903). One subtype (n = 345), characterised by mild liver enzyme elevation and coagulation abnormalities in late pregnancy, had the highest preterm birth rate (57.7%) and ICU admission rate (9.6%), and showed the largest aspirin-associated reduction in preterm birth (ATE −0.026, 95% CI: −0.032 to −0.021), consistent across 70/30 splits. Sensitivity analyses using 34 indicators confirmed similar effects (ATE −0.027, 95% CI: −0.031 to −0.023), while no benefit was observed in non-preeclamptic women. These findings suggest that aspirin prophylaxis may be associated with a greater reduction in preterm birth in this preeclampsia subtype, but further validation is required. Full article

The development of patient-specific vascular constructs is essential for advancing clinically effective 3D-bioprinted and tissue-engineered grafts. Optimizing cellular composition and promoting robust vascularization within engineered tissues is important for building an effective vascular construct. In cardiac and vascular patches in particular, insufficient microvascular development often leads to diffusion limitations, ischemia, and eventual necrosis, representing a major barrier to clinical translation. This review examines the challenges of designing constructs that are both anatomically functional and adequately vascularized. We discuss current strategies for tailoring cell ratios and enhancing vascular maturation, as well as emerging technologies in bioprinting, biomaterials, and computational design. Together, these innovations highlight future directions for creating grafts capable of functional integration and long-term survival in patients. Full article

Radiation-induced vaginal injury is an underrecognized complication of cervical cancer radiotherapy and evolves through a pathological continuum involving epithelial depletion, oxidative stress, chronic inflammation, microvascular dysfunction, extracellular-matrix remodeling, fibrosis, loss of tissue compliance, and vaginal stenosis. Current management remains largely supportive and is constrained by short local residence, limited anatomical adaptability, variable adherence, and insufficient control of radiation-specific tissue damage. This Mini-Review examines polymeric biomaterials for the prevention and repair of radiation-induced vaginal injury from a pathobiology–material–function perspective. Three complementary strategies are discussed: local polymeric formulations and mucoadhesive hydrogels for mucosal protection and sustained delivery; personalized dilators, stents, and adaptive devices for maintaining vaginal patency; and regenerative scaffolds and tissue-engineered constructs for restoring epithelial, vascular, stromal, and smooth-muscle compartments. Conventional hyaluronic-acid formulations have progressed furthest clinically, whereas responsive hydrogels, shape-adaptive devices, extracellular-matrix-derived materials, and cell- or exosome-based scaffolds remain predominantly preclinical. Importantly, much of the evidence derives from acute irradiation models, non-irradiated reconstruction studies, or engineering prototypes and therefore does not yet demonstrate durable reversal of chronic fibrosis or stenosis. Future development should prioritize stage-specific multifunctional interventions, fractionated irradiation models, standardized structural and functional outcomes, and rigorous oncological safety assessment. Acellular hydrogels, cell-free hybrid scaffolds, and removable drug-eluting devices may offer the most practical near-term translational routes. Full article

Carotid intima-media thickness (CIMT) is an established marker of subclinical atherosclerosis, but its reliance on ultrasound equipment and trained operators limits its suitability for large-scale screening. Retinal fundus photography may offer a more scalable approach to vascular assessment, as it contains retinal vascular features associated with systemic cardiovascular health and can be acquired quickly and non-invasively. This study proposes a two-stage transfer learning framework for CIMT-based cardiovascular risk classification. An attention-enhanced EfficientNet-B4 is first pretrained on the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection dataset and subsequently transferred to a weight-sharing Siamese architecture to classify the China Fundus Carotid Intima-Media Thickness dataset. Across ten independent initializations, the proposed model achieved an average macro-F1 score of 77.81% and an area under the receiver operating characteristic curve of 84.08% on the validation set and 78.29% and 85.22%, respectively, on the test set. It achieved overall higher point estimates for performance metrics than those previously reported for the Siamese ResNeXt baseline model with squeeze-and-excitation attention, and demonstrated a reduced disparity in class-wise performance. Grad-CAM analysis further revealed differences in the spatial activation patterns between models trained under different experimental configurations. Overall, these findings support the feasibility of fundus imaging as a scalable and non-invasive approach to CIMT-based cardiovascular risk assessment. Full article

Stroke is a leading cause of death and long-term disability, and auxiliary methods for rapid patient identification are needed. Previous studies based on electrocardiography (ECG) have attempted stroke classification using quantitative measurements and future stroke risk prediction using raw signals. However, measurements do not preserve all waveform detail, and models using raw signals alone may not sufficiently learn quantitative characteristics from limited data. We therefore propose a hybrid fusion framework that integrates the raw 12-lead signal and the quantitative measurements of the same ECG record. It combines a signal-only model, a feature-only model, and an interaction early fusion model that couples the two inputs at the raw data and representation levels, averaging their logits for the final prediction. Evaluation used 4147 ECG records from 4020 patients in a single-institution retrospective cohort, with patient-level splits. The proposed model achieved an F1-score of 77.6%, an area under the receiver operating characteristic curve (AUC) of 0.832, and a Brier score of 0.169. Among the evaluated models, the proposed framework achieved the highest AUC and lowest Brier score. The AUC improved by 0.034 over the best unimodal model, and an AUC of 0.820 was maintained under a year-based temporal holdout. These results suggest that integrating complementary representations of the same ECG at multiple levels can improve stroke classification. The framework could potentially serve as an auxiliary decision-support tool for identifying patients who require further evaluation, using ECG characteristics statistically associated with stroke. Full article

Sulfate-reducing bacteria (SRB) play crucial roles in microbiologically induced corrosion (MIC), especially in anaerobic environments where the production of hydrogen sulfide (H₂S) and biofilm formation accelerate metal degradation. Conventional treatments, such as THPS-based biocides, while effective, face limitations relating to the development of resistance and poor biofilm penetration. In this study, we evaluated the use of a non-specific bacteriophage cocktail as a biological control strategy against a mixed SRB culture (AP) biofilm and related corrosion, utilizing a pilot-scale system with AISI 1020 carbon steel coupons. Over 38 days, we monitored H₂S production, optical density, and ATP concentrations, along with the surface characterization of steel coupons through scanning electron microscopy (SEM) and profilometry. Metagenomic sequencing revealed changes in microbial community composition, including reductions in sulfate consumers and shifts in dominant taxa. SEM and profilometric analyses showed disrupted and less aggressive biofilms on treated surfaces, often exhibiting filamentous structures. Diversity indices indicated the modulation of microbial richness and composition over time. These findings demonstrate the potential of bacteriophage cocktails to reshape complex microbial communities and reduce MIC, providing a sustainable alternative to chemical biocides in large-scale industrial settings. Full article

Pulsed electromagnetic field (PEMF) increases the amplitude of muscle activity during physical exercise. This study investigated the effects of PEMF stimulation timing on neuromuscular activation in young sedentary males, during moderate-intensity constant-load cycling. We examined whether a PEMF applied at the beginning or during the middle of continuous exercise elicits differential effects on muscle activation. The participants performed a 30 min cycling task at 50% of VO₂max in which PEMF stimulation was alternately activated or deactivated in 15 min bouts. Surface electromyography (EMG) was recorded from the right vastus medialis and biceps femoris, and blood lactate samples were collected at baseline and during exercise. Data were analyzed via linear mixed-effects modeling. Active PEMF stimulation significantly increased normalized EMG amplitude in the vastus medialis (p < 0.001) and biceps femoris (p = 0.001) compared to inactive conditions, with the greatest effect observed when stimulation was applied after 15 min of exercise. Blood lactate concentrations were significantly elevated during PEMF application relative to baseline and inactive stimulation (p < 0.001). The timing of PEMF stimulation influenced neuromuscular responses during exercise, highlighting its potential as a tool to modulate muscle performance in sedentary individuals. Full article

The biomechanics of the vitreous humor undergo important changes in both normal aging and various pathologic conditions. While these changes are clinically visible, there are limited objective in vivo measurements of vitreous biomechanics. New imaging modalities have the potential to enable quantitative assessment of vitreous properties such as viscoelasticity in vivo, which could have relevance in assessing the risk of progression of vitreoretinal diseases. In this article, we discuss the current clinical approaches to vitreous biomechanics and review relevant new imaging methods with potential for clinical translation. Full article

Domain generalization (DG) for medical image segmentation is commonly approached by simulating domain shifts through deterministic image transformations or feature perturbations. However, such methods implicitly assume that unseen domains can be approximated by predefined appearance variations. In this work, we challenge this assumption and hypothesize that medical domain shifts are more fundamentally characterized as uncertainty in spectral distributions. While anatomical structures are largely preserved across institutions, scanners, and acquisition protocols, substantial variations arise in image appearance, texture, and artifacts, which are predominantly manifested in the spectral domain. To test this hypothesis, we propose Spectral-Distribution Uncertainty (SDU), a frequency-aware DG framework that explicitly models uncertainty in the Fourier amplitude distributions of intermediate representations. By decomposing Transformer features into amplitude and phase components, SDU disentangles domain-specific spectral characteristics from domain-invariant anatomical semantics. Instead of imposing handcrafted frequency perturbations, SDU estimates spectral-distribution uncertainty during training and injects stochastic variations into the amplitude space, synthesizing diverse yet anatomically consistent representations that emulate potential unseen domains. Consequently, SDU expands the support of spectral feature distributions, enabling the model to learn representations that are robust to uncertainty-induced spectral shifts. Extensive experiments on retinal fundus and prostate MRI benchmarks demonstrate that SDU consistently outperforms state-of-the-art DG methods. Beyond empirical gains, these findings provide evidence that medical domain shifts are more appropriately characterized as uncertainty in spectral feature distributions, offering a principled perspective for generalizable medical image segmentation. Full article

Background: Primary stability (PS) is a crucial determinant of long-term dental implant success, particularly in compromised or low-density bone sites. While macrogeometry is known to influence initial mechanical anchorage, the specific contribution of thread dimensions across varying bone densities remains incompletely understood. This in vitro study evaluated the influence of two different implant macrogeometries characterized by distinct thread configurations on primary implant stability within standardized polyurethane foam models representing different bone qualities. Methods: Two implant designs featuring distinct thread profiles were tested: the Evolution KRI (larger threads, pitch 1.8–2.2 mm, depth 0.55–1.025 mm) and the Evolution PRI (smaller threads, pitch 1.0 mm, depth 0.5 mm), across three fixture diameters (4.0, 4.5, and 5.0 mm). Implants were inserted into three standardized polyurethane block configurations: Group I (25 PCF uniform block, simulating D2 dense trabecular bone), Group II (10 PCF trabecular base with a 3 mm 40 PCF cortical layer, simulating D3 bone), and Group III (12.5 PCF trabecular base with a 2 mm 40 PCF cortical layer, simulating D4 bone). Each experimental group included 10 implants (n = 10). Two RFA measurements were collected for each implant and averaged prior to statistical analysis. Group comparisons were performed using one-way ANOVA followed by Tukey’s post hoc test. Primary stability was quantitatively assessed using Resonance Frequency Analysis (RFA) to record Implant Stability Quotient (ISQ) scores. Results: Statistically significant differences in ISQ values were observed among the experimental groups ($F=9.909,p<0.0001,R^2=0.4785$). Implants featuring larger threads (KRI) demonstrated consistently higher mean ISQ scores compared to smaller-thread implants (PRI) across all bone densities. In Group I (D2 model), KRI implants achieved a significantly higher mean ISQ than PRI implants (67.1 ± 1.91 vs. 65.1 ± 1.43; p = 0.0472). In Group II (D3 model with 3 mm cortical layer), this difference was most pronounced, with KRI reaching $68.5\pm 0.58$ compared to 64.8 ± 2.15 for PRI (p < 0.0001). In Group III (D4 model with 2 mm cortical layer), KRI maintained a higher stability trend ($68.0\pm 0.93$ vs. $66.5\pm 1.42$), though the inter-system difference did not reach statistical significance ($p=0.2401$). Inter-group comparisons confirmed that the highest overall primary stability was obtained by large-thread implants in the presence of a 3 mm cortical layer. Conclusions: Thread macrogeometry significantly influences primary implant stability; the tested KRI macrogeometry demonstrated higher ISQ values than the PRI macrogeometry under specific polyurethane configurations, particularly in intermediate trabecular–cortical bone structures. Full article

Early differentiation of neurodegenerative movement disorders remains challenging because of overlapping cognitive-behavioral profiles. The Montreal Cognitive Assessment (MoCA) is a widely used 30-point screening instrument; however, the extent to which MoCA subdomain scores alone can support automated multi-class differential diagnosis or score prediction requires rigorous methodological evaluation. In this study, we investigated supervised machine learning algorithms across two distinct tasks using clinical data from the National Institute of Neurological Disorders and Stroke (NINDS) Parkinson’s Disease Biomarkers Program ($N=64$): (1) multi-class classification across movement disorder diagnoses, including Parkinson’s disease (PD, $n=27$), No Neurological Diagnosis (Controls, $n=14$), Progressive Supranuclear Palsy (PSP, $n=9$), Essential Tremor (ET, $n=8$), Corticobasal Degeneration (CBD, $n=1$), Multiple System Atrophy (MSA, $n=1$), and Other ($n=4$); and (2) numerical prediction/reconstruction of the MoCA total score from its constitutive domain items, with external validation on 1499 records from the Parkinson’s Progression Markers Initiative (PPMI). Under stratified 5-fold cross-validation, multi-class diagnostic classification was severely limited: the highest macro-averaged F1-score achieved by any trained model was 0.362 (95% CI: 0.284–0.440; Logistic Regression), trailing a naive Constant majority-class baseline in classification accuracy (0.463 vs. 0.366). Bootstrap resampling yielded apparent increases in performance (Neural Network macro-F1: 0.558, 95% CI: 0.512–0.604; AdaBoost: 0.555); however, this reflects optimistic bias from pseudo-replicate sampling rather than genuine clinical generalizability. For continuous score modeling, Linear Regression and Stochastic Gradient Descent achieved near-perfect internal fit ($R^2=1.000$ and 0.996, respectively), and retained near-perfect performance in the external PPMI cohort ($R^2=0.992$ and 0.991; MSE $\le 0.077$). We demonstrate that this regression success reflects the mathematical recovery of an additive scoring definition, i.e., target leakage, rather than independent clinical prediction. MoCA domain scores alone possess insufficient disease-specific variance to differentiate complex parkinsonian syndromes, underscoring that automated clinical classification requires the integration of multimodal neuroimaging, fluid, and digital motor biomarkers. Full article