Search Results (1,614)

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

Background and Objectives: Although survival after congenital cardiac malformations (CCM) has improved, little is known about Romanian children’s own perceptions of health-related quality of life (HRQoL) or their emotional burden. We compared HRQoL, depressive symptoms, and anxiety across lesion severity strata and explored clinical predictors of impaired HRQoL. Methods: In this cross-sectional study (1 May 2023–30 April 2025), 72 children (mean age 7.9 ± 3.0 years, 52.8% male) attending a tertiary cardiology clinic completed the Romanian-validated Pediatric Quality of Life Inventory (PedsQL), Children’s Depression Inventory (CDI) and the Screen for Child Anxiety-Related Emotional Disorders questionnaire (SCARED-C, child version). Lesions were classified as mild (n = 22), moderate (n = 34), or severe (n = 16). Left-ventricular ejection fraction (LVEF) and unplanned cardiac hospitalisations over the preceding 12 months were extracted from electronic records. Results: Mean PedsQL total scores declined stepwise by severity (mild 80.9 ± 7.3; moderate 71.2 ± 8.4; severe 63.1 ± 5.4; p < 0.001). CDI and SCARED-C scores rose correspondingly (CDI: 9.5 ± 3.0, 13.6 ± 4.0, 18.0 ± 2.7; anxiety: 15.2 ± 3.3, 17.2 ± 3.8, 24.0 ± 3.4; both p < 0.001). PedsQL correlated positively with LVEF (r = 0.51, p < 0.001) and negatively with hospitalisations (r = −0.39, p = 0.001), depression (r = −0.44, p < 0.001), and anxiety (r = −0.47, p < 0.001). In multivariable analysis, anatomical severity remained the sole independent predictor of lower HRQoL (β = −8.4 points per severity tier, p < 0.001; model R² = 0.45). Children with ≥ 1 hospitalisation (n = 42) reported poorer HRQoL (69.6 ± 8.0 vs. 76.1 ± 11.1; p = 0.005) and higher depressive scores (p < 0.001). Conclusions: HRQoL and internalising symptoms in Romanian children with CCM worsen with increasing anatomical complexity and recent hospital utilisation. The severity tier outweighed functional markers as the main determinant of HRQoL, suggesting that psychosocial screening and support should be scaled to lesion complexity. Integrating the routine use of the Romanian-validated PedsQL, CDI, and SCARED-C questionnaire into cardiology follow-up may help identify vulnerable patients early and guide targeted interventions. Full article

Background/Objectives: The simulation of human movement offers transformative potential for the design of medical devices, particularly in understanding the cause–effect dynamics in individuals with neurological or musculoskeletal impairments. This study presents a simulation-driven framework to determine the optimal ankle–foot orthosis (AFO) stiffness for mitigating the risk of ankle sprains due to excessive subtalar inversion during high-impact activities, such as landing from a free fall. Methods: We employed biomechanical simulations to assess the influence of translational stiffness on subtalar inversion control, given that inversion angles exceeding 25 degrees are strongly correlated with injury risk. Simulations were conducted using a musculoskeletal model with and without a passive AFO; the stiffness varied in three anatomical directions. A Design of Experiments (DoE) approach was utilized to capture nonlinear interactions among stiffness parameters. Results: The results indicated that increased translational stiffness significantly reduced inversion angles to safer levels, though direction–dependent effects were noted. Based on these insights, we developed a 4D visualization tool that integrates simulation data with an interactive color–coded interface to depict ”safe design” zones for various AFO stiffness configurations. This tool supports clinicians in selecting stiffness values that optimize both safety and functional performance. Conclusions: The proposed framework enhances clinical decision-making and engineering processes by enabling more accurate and individualized AFO designs. Full article

Objectives: Automatic tip localization is critical in ultrasound (US)-guided interventions. Although deep learning (DL) has been widely used for precise tip detection, existing methods are limited by the availability of real puncture data and expert annotations. Methods: To address these challenges, we propose a novel method that uses synthetic US puncture data to pre-train DL-based tip detectors, improving their generalization. Synthetic data are generated by fusing clinical US images of healthy controls with tips created using generative DL models. To ensure clinical diversity, we constructed a dataset from scans of 20 volunteers, covering 20 organs or anatomical regions, obtained with six different US machines and performed by three physicians with varying expertise levels. Tip diversity is introduced by generating a wide range of synthetic tips using a denoising probabilistic diffusion model (DDPM). This method synthesizes a large volume of diverse US puncture data, which are used to pre-train tip detectors, followed by subsequently training with real puncture data. Results: Our method outperforms MSCOCO pre-training on a clinical puncture dataset, achieving a 1.27–7.19% improvement in AP_0.1:0.5 with varying numbers of real samples. State-of-the-art detectors also show performance gains of 1.14–1.76% when applying the proposed method. Conclusions: The experimental results demonstrate that our method enhances the generalization of tip detectors without relying on expert annotations or large amounts of real data, offering significant potential for more accurate visual guidance during US-guided interventions and broader clinical applications. Full article

Fish coloration plays an important role in reproduction and camouflage, yet capturing color variation under field conditions remains challenging. We present a standardized, semi-automated protocol for measuring body coloration in the popular model fish threespine stickleback (Gasterosteus aculeatus). Individuals are photographed in a controlled light box within minutes of capture, and color is sampled from eight anatomically defined standard sites in human-perception-based CIELAB space. Analyses combine univariate color metrics, multivariate statistics, and the ΔE* perceptual difference index to detect subtle shifts in hue and brightness. Validation on pre-spawning fish shows the method reliably distinguishes males and females well before full breeding colors develop. Although it currently omits ultraviolet signals and fine-scale patterning, the approach scales efficiently to large sample sizes and varying lighting conditions, making it well suited for population-level surveys of camouflage dynamics, sexual dimorphism, and environmental influences on coloration in sticklebacks. Full article

Introduction: Given the impact of aneurysmal subarachnoid hemorrhage (aSAH) on patients’ health, preclinical research is substantial to understand its pathophysiology and improve treatment strategies, which necessitates reliable and comprehensive animal models. Traditionally, aSAH models utilize iliac or subclavian access for angiography, requiring invasive procedures that are associated with significant risks and animal burden. This pilot study explores a less invasive method of digital subtraction angiography (DSA) by using the auricular artery (AA) as an alternative access point. Our aim was to demonstrate the feasibility of this refined technique, with the intention of reducing procedural risks, providing shorter operation times with enhanced neurological recovery, and simplifying the process for both researchers and animals. Materials and Methods: In this study, six female New Zealand white rabbits (3.2–4.1 kg body weight) underwent experimental induction of aSAH via a subclavian-cisternal shunt. The initial steps of this procedure followed traditional techniques, consisting of subclavian access through microsurgical preparation, followed by DSA to analyze retrograde filling of the basilar artery (BA). To evaluate the alternative method, on day 3 after induction of aSAH, DSA was performed via the AA instead of the traditional subclavian or femoral access. A catheter was placed in the AA to allow retrograde filling of the BA. This approach aimed to simplify the procedure while maintaining comparable imaging quality. Results: All rabbits survived until the study endpoint. Postoperatively, two rabbits showed signs of hemisyndrome, which significantly improved by the time of follow-up. No additional morbidities were observed. Upon euthanasia and necropsy, all animals showed clear subarachnoid bleeding patterns. DSA via the AA produced strong contrasting of the BA comparable to the traditional method. Conclusions: This technical note presents an initial evaluation of AA access as a feasible and potentially advantageous method for DSA in a rabbit model of blood shunt subarachnoid hemorrhage. The method shows promise in reducing invasiveness and procedural complexity, but further studies are required to fully establish its efficacy and safety. Future research should focus on expanding the sample size, refining the anatomical understanding of the AA, and continuing to align with ethical considerations regarding animal welfare. Full article

Background/Objectives: Multimodal large language models (LLMs) are increasingly used in radiology. However, their ability to recognize fundamental imaging features, including modality, anatomical region, imaging plane, contrast-enhancement status, and particularly specific magnetic resonance imaging (MRI) sequences, remains underexplored. This study aims to evaluate and compare the performance of three advanced multimodal LLMs (ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro) in classifying brain MRI sequences. Methods: A total of 130 brain MRI images from adult patients without pathological findings were used, representing 13 standard MRI series. Models were tested using zero-shot prompts for identifying modality, anatomical region, imaging plane, contrast-enhancement status, and MRI sequence. Accuracy was calculated, and differences among models were analyzed using Cochran’s Q test and McNemar test with Bonferroni correction. Results: ChatGPT-4o and Gemini 2.5 Pro achieved 100% accuracy in identifying the imaging plane and 98.46% in identifying contrast-enhancement status. MRI sequence classification accuracy was 97.7% for ChatGPT-4o, 93.1% for Gemini 2.5 Pro, and 73.1% for Claude 4 Opus (p < 0.001). The most frequent misclassifications involved fluid-attenuated inversion recovery (FLAIR) sequences, often misclassified as T1-weighted or diffusion-weighted sequences. Claude 4 Opus showed lower accuracy in susceptibility-weighted imaging (SWI) and apparent diffusion coefficient (ADC) sequences. Gemini 2.5 Pro exhibited occasional hallucinations, including irrelevant clinical details such as “hypoglycemia” and “Susac syndrome.” Conclusions: Multimodal LLMs demonstrate high accuracy in basic MRI recognition tasks but vary significantly in specific sequence classification tasks. Hallucinations emphasize caution in clinical use, underlining the need for validation, transparency, and expert oversight. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Accurate segmentation of cardiac MR images using deep neural networks is crucial for cardiac disease diagnosis and treatment planning, as it provides quantitative insights into heart anatomy and function. However, achieving high segmentation accuracy relies heavily on extensive, precisely annotated datasets, which are costly and time-consuming to obtain. This study addresses this challenge by proposing a novel data augmentation framework based on a condition-guided diffusion generative model, controlled by multiple cardiac labels. The framework aims to expand annotated cardiac MR datasets and significantly improve the performance of downstream cardiac segmentation tasks. The proposed generative data augmentation framework operates in two stages. First, a Label Diffusion Module is trained to unconditionally generate realistic multi-category spatial masks (encompassing regions such as the left ventricle, interventricular septum, and right ventricle) conforming to anatomical prior probabilities derived from noise. Second, cardiac MR images are generated conditioned on these semantic masks, ensuring a precise one-to-one mapping between synthetic labels and images through the integration of a spatially-adaptive normalization (SPADE) module for structural constraint during conditional model training. The effectiveness of this augmentation strategy is demonstrated using the U-Net model for segmentation on the enhanced 2D cardiac image dataset derived from the M&M Challenge. Results indicate that the proposed method effectively increases dataset sample numbers and significantly improves cardiac segmentation accuracy, achieving a 5% to 10% higher Dice Similarity Coefficient (DSC) compared to traditional data augmentation methods. Experiments further reveal a strong correlation between image generation quality and augmentation effectiveness. This framework offers a robust solution for data scarcity in cardiac image analysis, directly benefiting clinical applications. Full article

In recent years, the concept of the myofascial network has transformed biomechanical understanding by emphasizing the body as an integrated, multidirectional system. This study advances that paradigm by applying graph theory to model the osteo-myofascial system as an anatomical network, enabling the identification of topologically central nodes involved in force transmission, stability, and coordination. Using the aNETomy model and the BIOMECH 3.4 database, we constructed an undirected network of 2208 anatomical nodes and 7377 biomechanical relationships. Centrality analysis (degree, betweenness, and closeness) revealed that structures such as the sacrum and thoracolumbar fascia exhibit high connectivity and strategic importance within the network. These findings, while derived from a theoretical modeling approach, suggest that such key nodes may inform targeted treatment strategies, particularly in complex or compensatory musculoskeletal conditions. The proposed concept of a polyconnective skeleton (PCS) synthesizes the most influential anatomical hubs into a functional core of the system. This framework may support future clinical and technological applications, including integration with imaging modalities, real-time monitoring, and predictive modeling for personalized and preventive medicine. Full article

Optical clearing agents (OCAs) offer a promising approach to enhance skin transparency by reducing scattering and improving photon transmission, which is critical for non-invasive optical diagnostics such as glucose sensing and vascular imaging. However, the complex multilayered structure of skin and anatomical variability across different regions pose challenges for accurately evaluating OCA performance. In this study, we developed a multilayer Monte Carlo (MC) simulation model integrated with a depth- and time-resolved diffusion model based on Fick’s law to quantitatively assess the combined effects of OCA penetration depth and refractive index change on optical clearing. The model incorporates realistic skin parameters, including variable stratum corneum thicknesses, and was validated through in vivo experiments using glycerol and glucose at different concentrations. Both the simulation and experimental results demonstrate that increased stratum corneum thickness significantly reduces blood absorption of light and lowers the clearing efficiency of OCAs. The primary influence of stratum corneum thickness lies in requiring a greater degree of refractive index matching rather than necessitating a deeper OCA penetration depth to achieve effective optical clearing. These findings underscore the importance of considering regional skin differences when selecting OCAs and designing treatment protocols. This work provides quantitative insights into the interaction between tissue structure and optical response, supporting improved application strategies in clinical diagnostics. Full article

Background/Objectives: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomical and biological changes. Methods: We propose a decision tree offline adaptation framework based on real-time assessments of Activity Concentration (AC), Normalized Target Signal (NTS), and bounded dose-volume histogram (bDVH%) metrics. Three offline strategies were developed: (1) preemptive adaptation for minor changes, (2) partial re-simulation for moderate changes, and (3) full re-simulation for major anatomical or metabolic alterations. Two clinical cases demonstrating strategies 1 and 2 are presented. Results: The preemptive adaptation strategy was applied in a case with early tumor shrinkage, maintaining delivery parameters within acceptable limits while updating contours and dose distribution. In the partial re-Simulation case, significant changes in PET signal necessitated a same-day PET functional modeling session and plan re-optimization, effectively restoring safe deliverability. Both cases showed reduced target volumes and improved OAR sparing without additional patient visits or tracer injections. Conclusions: Offline adaptive workflows for BgRT provide practical solutions to address inter-fractional changes in tumor structure and function. These strategies can help maintain the safety and accuracy of BgRT delivery and support clinical adoption of PET-guided radiotherapy, paving the way for future online adaptive capabilities. Full article

Background: Inadequate presurgical planning is a key contributor to suboptimal outcomes in orthognathic surgery. This study aims to assess the accuracy of a digital surgical planning workflow conducted prior to any orthodontic intervention. Methods: Digital planning was performed for 26 patients before orthodontic treatment (T0) and compared to the actual preoperative planning (T1). Digitized plaster casts were merged with CBCT data and converted to orthodontic setups to create a 3D virtual head model. After voxel-based registration of T0 and T1, dental arches were virtually osteotomized and repositioned according to planned outcomes. These T0 segments were then aligned with T1 planning using bony landmarks of the maxilla. Anatomical landmarks were used to construct virtual triangles on maxillary and mandibular segments, enabling assessment of positional and orientational differences. Transformations between T0 and T1 were translated into clinically meaningful metrics. Results: Significant differences were found between T0 and T1 at the dental level. T1 exhibited a greater clockwise rotation of the dental maxilla (mean: 2.85°) and a leftward translation of the mandibular dental arch (mean: 1.19 mm). In SARME cases, the bony mandible showed larger anti-clockwise roll differences. Pitch variations were also more pronounced in maxillary extraction cases, with both the dental maxilla and bony mandible demonstrating increased clockwise rotations. Conclusions: The proposed orthognathic surgical planning workflow shows potential for simulating mandibular outcomes but lacks dental-level accuracy, especially in maxillary anterior torque. While mandibular bony outcome predictions align reasonably with pretreatment planning, notable discrepancies exceed clinically acceptable thresholds. Current accuracy limits routine use; further refinement and validation in larger, homogeneous patient groups are needed to enhance clinical reliability and applicability. Full article

Three-dimensional imaging technologies are increasingly used in breast reconstructive and plastic surgery due to their potential for efficient and accurate preoperative assessment and planning. This study systematically evaluates the accuracy and consistency of six commercially available 3D scanning applications (apps)—Structure Sensor, 3D Scanner App, Heges, Polycam, SureScan, and Kiri—in reconstructing the female torso. To avoid variability introduced by human subjects, a silicone breast mannequin model was scanned, with fiducial markers placed at known anatomical landmarks. Manual distance measurements were obtained using calipers by two independent evaluators and compared to digital measurements extracted from 3D reconstructions in Blender software. Each scan was repeated six times per application to ensure reliability. SureScan demonstrated the lowest mean error (2.9 mm), followed by Structure Sensor (3.0 mm), Heges (3.6 mm), 3D Scanner App (4.4 mm), Kiri (5.0 mm), and Polycam (21.4 mm), which showed the highest error and variability. Even the app using an external depth sensor (Structure Sensor) showed no statistically significant accuracy advantage over those using only the iPad’s built-in camera (except for Polycam), underscoring that software is the primary driver of performance, not hardware (alone). This work provides practical insights for selecting mobile 3D scanning tools in clinical workflows and highlights key limitations, such as scaling errors and alignment artifacts. Future work should include patient-based validation and explore deep learning to enhance reconstruction quality. Ultimately, this study lays the foundation for more accessible and cost-effective 3D imaging in surgical practice, showing that smartphone-based tools can produce clinically useful scans. Full article

Clinical and animal studies suggest that multiple brain systems are involved in mediating reward-motivated and related emotional behavior including the consumption of commonly used drugs and palatable food, and there is evidence that the repeated ingestion of or exposure to these rewarding substances may in turn stimulate these brain systems to produce an overconsumption of these substances along with co-occurring emotional disturbances. To understand this positive feedback loop, this review focuses on a specific population of hypothalamic peptide neurons expressing melanin-concentrating hormone (MCH), which are positively related to dopamine reward and project to forebrain areas that mediate this behavior. It also examines neurons expressing the peptide hypocretin/orexin (HCRT) that are anatomically and functionally linked to MCH neurons and the molecular systems within these peptide neurons that stimulate their development and ultimately affect behavior. This report first describes evidence in animals that exposure in adults and during adolescence to rewarding substances, such as the drugs alcohol, nicotine and cocaine and palatable fat-rich food, stimulates the expression of MCH as well as HCRT and their intracellular molecular systems. It also increases reward-seeking and emotional behavior, leading to excess consumption and abuse of these substances and neurological conditions, completing this positive feedback loop. Next, this review focuses on the model involving embryonic exposure to these rewarding substances. In addition to revealing a similar positive feedback circuit, this model greatly advances our understanding of the diverse changes that occur in these neuropeptide/molecular systems in the embryo and how they relate, perhaps causally, to the disturbances in behavior early in life that predict a later increased risk of developing substance use disorders. Studies using this model demonstrate in animals that embryonic exposure to these rewarding substances, in addition to stimulating the expression of peptide neurons, increases the intracellular molecular systems in neuroprogenitor cells that promote their development. It also alters the morphology, migration, location and neurochemical profile of the peptide neurons and causes them to develop aberrant neuronal projections to forebrain structures. Moreover, it produces disturbances in behavior at a young age, which are sex-dependent and occur in females more than in males, that can be directly linked to the neuropeptide/molecular changes in the embryo and predict the development of behavioral disorders later in life. These results supporting the close relationship between the brain and behavior are consistent with clinical studies, showing females to be more vulnerable than males to developing substance use disorders with co-occurring emotional conditions and female offspring to respond more adversely than male offspring to prenatal exposure to rewarding substances. It is concluded that the continued consumption of or exposure to rewarding substances at any stage of life can, through such peptide brain systems, significantly increase an individual’s vulnerability to developing neurological disorders such as substance use disorders, anxiety, depression, or cognitive impairments. Full article