Search Results (243)

Pathological conditions of the spinal cord have been found to be associated with cervical spondylotic myelopathy (CSM). This study aims to explore the feasibility of automatic deep-learning-based classification of the pathological condition of the spinal cord to quantify its severity. A Diffusion Tensor Imaging (DTI)-based spinal cord pathological assessment method was proposed. A multi-dimensional feature fusion model, referred to as DCSANet-MD (DTI-Based CSM Severity Assessment Network-Multi-Dimensional), was developed to extract both 2D and 3D features from DTI slices, incorporating a feature integration mechanism to enhance the representation of spatial information. To evaluate this method, 176 CSM patients with cervical DTI slices and clinical records were collected. The proposed assessment model demonstrated an accuracy of 82% in predicting two categories of severity levels (mild and severe). Furthermore, in a more refined three-category severity classification (mild, moderate, and severe), using a hierarchical classification strategy, the model achieved an accuracy of approximately 68%, which significantly exceeded the baseline performance. In conclusion, these findings highlight the potential of the deep-learning-based method as a decision-making support tool for DTI-based pathological assessments of CSM, offering great value in monitoring disease progression and guiding the intervention strategies. Full article

Background/Objectives: This study investigated variations in DNA methylation patterns associated with chronic pain and propensity for recurrent pressure injuries (PrI) in persons with spinal cord injury (SCI). Methods: Whole blood was collected from 81 individuals with SCI. DNA methylation was quantified using Illumina genome-wide arrays (EPIC and EPICv2). Comprehensive clinical profiles collected included secondary health complications, in particular current PrI and chronic pain. Relationships between recurrent PrI and chronic pain and whether the co-occurrence of both traits was mediated by changes in DNA methylation were investigated using R packages limma, DMRcate and mCSEA. Results: Three differentially methylated positions (DMPs) (cg09867095, cg26559694, cg24890286) and one region in the micro-imprinted locus for BLCAP/NNAT are associated with chronic pain in persons with SCI. The study cohort was stratified by PrI status to identify any sites associated with chronic pain and while the same three sites and region were replicated in the group with no recurrent PrI, two novel, hypermethylated (cg21756558, cg26217441) sites and one region in the protein-coding gene FDFT1 were identified in the group with recurrent PrI. Gene enrichment and genes associated with specific promoters using MetaScape identified several shared disorders and ontology terms between independent phenotypes of pain and recurrent PrI and interactive sub-groups. Conclusions: DMR analysis using mCSEA identified several shared genes, promoter-associated regions and CGI associated with overall pain and PrI history, as well as sub-groups based on recurrent PrI history. These findings suggest that a much larger gene regulatory network is associated with each phenotype. These findings require further validation. Full article

Background: Chronic postsurgical pain (CPSP) poses a major clinical challenge due to unresolved links between neurotrophic pathways and endoplasmic reticulum (ER) stress. While Neurotrophic Tyrosine Kinase Receptor Type 1 (NTRK1) modulates ER stress in neuropathic pain, its interaction with Insulin-Like Growth Factor II (IGF2) in CPSP remains uncharacterized, impeding targeted therapy. This study defined the spinal NTRK1-IGF2-ER stress axis in CPSP. Methods: Using a skin/muscle incision–retraction (SMIR) rat model, we integrated molecular analyses and intrathecal targeting of NTRK1 (GW441756) or IGF2 (siRNA). Results: SMIR surgery upregulated spinal NTRK1, IGF2, and ER stress mediators. NTRK1 inhibition reduced both NTRK1/IGF2 expression and ER stress, reversing mechanical allodynia. IGF2 silencing attenuated ER stress and pain but did not affect NTRK1, revealing a unidirectional signaling cascade where NTRK1 drives IGF2-dependent ER stress amplification. These findings expand understanding of stress-response networks in chronic pain. Conclusions: We show that spinal NTRK1 drives IGF2-mediated ER stress to sustain CPSP. The NTRK1-IGF2-ER stress axis represents a novel therapeutic target; NTRK1 inhibitors and IGF2 biologics offer non-opioid strategies for precision analgesia. This work advances CPSP management and demonstrates how decoding unidirectional signaling hierarchies can transform neurological disorder interventions. Full article

Diffusion Tensor Imaging (DTI) is a crucial imaging technique for accurately assessing pathological changes in Cervical Spondylotic Myelopathy (CSM). However, the segmentation of spinal cord DTI images primarily relies on manual methods, which are labor-intensive and heavily dependent on the subjective experience of clinicians, and existing research on DTI automatic segmentation cannot fully satisfy clinical requirements. Thus, this poses significant challenges for DTI-assisted diagnostic decision-making. This study aimed to deliver AI-driven segmentation for spinal cord DTI. To achieve this goal, a comparison experiment of candidate input features was conducted, with the preliminary results confirming the effectiveness of applying a diffusion-free image (B0 image) for DTI segmentation. Furthermore, a deep-learning-based model, named SCS-Net (Spinal Cord Segmentation Network), was proposed accordingly. The model applies a classical U-shaped architecture with a lightweight feature extraction module, which can effectively alleviate the training data scarcity problem. The proposed method supports eight-region spinal cord segmentation, i.e., the lateral, dorsal, ventral, and gray matter areas on the left and right sides. To evaluate this method, 89 CSM patients from a single center were collected. The model demonstrated satisfactory accuracy for both general segmentation metrics (precision, recall, and Dice coefficient) and a DTI-specific feature index. In particular, the proposed model’s error rate for the DTI-specific feature index was evaluated as 5.32%, 10.14%, 7.37%, and 5.70% on the left side, and 4.60%, 9.60%, 8.74%, and 6.27% on the right side of the spinal cord, respectively, affirming the model’s consistent performance for radiological rationality. In conclusion, the proposed AI-driven segmentation model significantly reduces the dependence on DTI manual interpretation, providing a feasible solution that can improve potential diagnostic outcomes for patients. Full article

The gut–brain axis (GBA) refers to the biochemical bidirectional communication between the central nervous system (CNS) and the gastrointestinal tract, linking brain and gut functions. It comprises a complex network of interactions involving the endocrine, immune, autonomic, and enteric nervous systems. The balance of this bidirectional pathway depends on the composition of the gut microbiome and its metabolites. While the causes of neurodegenerative diseases (NDDs) vary, the gut microbiome plays a crucial role in their development and prognosis. NDDs are often associated with an inflammation-related gut microbiome. However, restoring balance to the gut microbiome and reducing inflammation may have therapeutic benefits. In particular, introducing short-chain fatty acid-producing bacteria, key metabolites that support gut homeostasis, can help counteract the inflammatory microbiome. This strong pathological link between the gut and NDDs underscores the gut–brain axis (GBA) as a promising target for therapeutic intervention. This review, by scrutinizing the more recent original research articles published in PubMed (MEDLINE) database, emphasizes the emerging notion that GBA is an equally important pathological marker for neurological movement disorders, particularly in Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis, Huntington’s disease and neurotraumatic disorders such as traumatic brain injury and spinal cord injury. Additionally, the GBA presents a promising therapeutic target for managing these diseases. Full article

Single-cell RNA sequencing (scRNA-seq) has revolutionized neuroscience by enabling the analysis of cellular heterogeneity and dynamic molecular processes at the single-cell resolution. In spinal cord research, scRNA-seq provides critical insights into cell type diversity, developmental trajectories, and pathological mechanisms. This review summarizes recent progress in the application of scRNA-seq to spinal cord development, injury, and neurodegenerative diseases and discusses the current challenges and future directions. Relevant studies focusing on the key applications of scRNA-seq, including advances in spatial transcriptomics and multi-omics integration, were retrieved from PubMed and the Web of Science. scRNA-seq has enabled the identification of distinct spinal cord cell populations and revealed the gene regulatory networks driving development. Injury models have revealed the temporal dynamics of immune and glial responses, alongside potential regenerative processes. In neurodegenerative conditions, scRNA-seq highlights cell-specific vulnerabilities and molecular changes. The integration of spatial transcriptomics and computational tools, such as machine learning, has further improved the resolution of spinal cord biology. However, challenges remain in terms of data complexity, sample acquisition, and clinical translation. Single-cell transcriptomics is a powerful approach for understanding spinal cord biology. Its integration with emerging technologies will advance both basic research and clinical applications, supporting personalized and regenerative therapy. Addressing these technical and analytical barriers is essential to fully realize the potential of scRNA-seq in spinal cord science. Full article

Objective: This study aims to apply different deep learning convolutional neural network algorithms to assess the grading of cervical spinal stenosis and to evaluate their consistency with clinician grading results as well as clinical manifestations of patients. Methods: We retrospectively enrolled 954 patients with cervical spine magnetic resonance imaging (MRI) data and medical records from the Fifth Affiliated Hospital of Sun-Yat Sen University. The Kang grading method for sagittal MR images of the cervical spine and the spinal cord compression ratio for horizontal MR images of the cervical spine were adopted for cervical spinal canal stenosis grading. The collected data were randomly divided into training/validation and test sets. The training/validation sets were processed by various image preprocessing and annotation methods, in which deep learning convolutional networks, including classification, target detection, and key point localization models, were applied. The predictive grading of the test set by the model was finally contrasted with the grading results of the clinicians, and correlation analysis was performed with the clinical manifestations of the patients. Result: The EfficientNet_B5 model achieved a five-fold cross-validated accuracy of 79.45% and near-perfect agreement with clinician grading on the test set (κ= 0.848, 0.822), surpassing resident–clinician consistency (κ = 0.732, 0.702). The model-derived compression ratio (0.45 ± 0.07) did not differ significantly from manual measurements (0.46 ± 0.07). Correlation analysis showed moderate associations between model outputs and clinical symptoms: EfficientNet_B5 grades (r = 0.526) were comparable to clinician assessments (r = 0.517, 0.503) and higher than those of residents (r = 0.457, 0.448). Conclusion: CNN models demonstrate strong performance in the objective, consistent, and efficient grading of cervical spinal stenosis severity, offering potential clinical value in automated diagnostic support. Full article

Spinal cord injury (SCI) is one of the most debilitating conditions that has profound effects on every physiological system, including respiratory dysfunction, which is listed among the most common causes of mortality and morbidity in this population. Previous research has demonstrated that respiratory training could facilitate respiratory motor- and autonomic activity-based plasticity. However, due to the reduced excitability of spinal networks below the level of injury, the effectiveness of such interventions is often limited to the residual functional capacity preserved after injury. In recent decades, several novel neuromodulatory techniques have been explored to enhance neuronal connectivity and integrate into respiratory rehabilitation strategies. In this review, we examine the mechanisms underlying respiratory deficits following SCI and discuss the neuromodulatory approaches designed to promote neural plasticity for respiratory recovery. Current evidence suggests that integrating multimodal neuromodulation with activity-based respiratory training holds promise; it may significantly enhance respiratory functional recovery and could become a standard component of respiratory rehabilitation protocols in individuals with SCI. Full article

Background and Objectives: Artificial intelligence (AI) has seen rapid integration into various areas of medicine, particularly with the advancement of machine learning (ML) and deep learning (DL) techniques. In pediatric orthopedics, the adoption of AI technologies is emerging but still not comprehensively reviewed. The purpose of this study is to review the latest evidence on the applications of artificial intelligence in the field of pediatric orthopedics. Materials and Methods: A literature search was conducted using PubMed and Web of Science databases to identify peer-reviewed studies published up to March 2024. Studies involving AI applications in pediatric orthopedic conditions—including spinal deformities, hip disorders, trauma, bone age assessment, and limb discrepancies—were selected. Eligible articles were screened and categorized based on application domains, AI models used, datasets, and reported outcomes. Results: AI has been successfully applied across several pediatric orthopedic subspecialties. In spinal deformities, models such as support vector machines and convolutional neural networks achieved over 90% accuracy in classification and curve prediction. For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. In trauma care, object detection models like YOLO and ResNet-based classifiers showed excellent sensitivity and specificity in pediatric fracture detection. Bone age estimation using DL models often matched or outperformed traditional methods. However, most studies lacked external validation, and many relied on small or single-institution datasets. Concerns were also raised about image quality, data heterogeneity, and clinical integration. Conclusions: AI holds significant potential to enhance diagnostic accuracy and decision making in pediatric orthopedics. Nevertheless, current research is limited by methodological inconsistencies and a lack of standardized validation protocols. Future efforts should focus on multicenter data collection, prospective validation, and interdisciplinary collaboration to ensure safe and effective clinical integration. Full article

This study aims to explore the potential of exergaming (which can be used along with prescriptive medication for children with spinal muscular atrophy) and examine its effects on monitoring and diagnosis. The present study focuses on comparing models trained on joint data for gesture detection, which has not been extensively explored in previous studies. The study investigates three approaches to detect gestures based on 3D Microsoft Azure Kinect joint data. We discuss simple decision rules based on angles and distances to label gestures. In addition, we explore supervised learning methods to increase the accuracy of gesture recognition in gamification. The compared models performed well on the recorded sample data, with the recurrent neural networks outperforming feedforward neural networks and decision trees on the captured motions. The findings suggest that gesture recognition based on joint data can be a valuable tool for monitoring and diagnosing children with spinal muscular atrophy. This study contributes to the growing body of research on the potential of virtual solutions in rehabilitation. The results also highlight the importance of using joint data for gesture recognition and provide insights into the most effective models for this task. The findings of this study can inform the development of more accurate and effective monitoring and diagnostic tools for children with spinal muscular atrophy. Full article

Background/Objectives: Surgical site infections (SSIs) in spine surgery pose significant risks, including neurological deficits, prolonged hospital stays, and increased healthcare costs. SSIs are classified by their location and include superficial, deep, and organ/space (OS) infections. In spine surgery, OS SSIs include osteomyelitis, discitis, and spinal epidural abscess. These infections are difficult to treat with conservative measures, impart significant morbidity, and incur increasing hospital costs. Despite advancements in surgical technique and infection control, the literature is conflicting on which factors are associated with a significant increase in risk of SSIs after spinal surgery. There is also a significant gap in the literature in defining the risk factors specific to OS SSIs. This study aims to identify risk factors associated with SSI after spine surgery at a single institution, as well as provide descriptive characteristics of patients with OS SSIs. Methods: This retrospective study analyzed spinal surgeries performed at a multi-center, single-institution between 1 January 2019 and 9 February 2025. Neurosurgical patients who underwent spine surgery were identified by ICD-10 procedure and diagnosis codes. Surgical infections were classified based on the National Healthcare Safety Network (NHSN) criteria. Univariate and multivariate analyses were performed to assess associations between patient demographics, comorbidities, and infection risk. Results: Of the 2363 unique spinal surgery patients, 39 developed infections, with 14 meeting the NHSN criteria for OS SSI. The overall rate of SSIs at this institution was 1.65%. Significant risk factors for developing an SSI included cardiovascular disease (p = 0.017) and COPD (p = 0.012). Multivariate analysis confirmed both risk factors identified in the univariate analysis as independent risk factors, with adjusted odds ratios of 1.97 (p = 0.033) and 2.072 (p = 0.041), respectively. The commonly cultured pathogens included Staphylococcus aureus, Staphylococcus epidermidis, and methicillin-resistant Staphylococcus aureus. Conclusions: Male sex, diabetes mellitus, gastroesophageal reflux disease, hyperlipidemia, hypertension, hardware placement, and a history of smoking were more common in patients with SSI. In the OS SSI subgroup, cardiovascular disease and COPD were associated with an increased risk of developing an OS SSI. Future research is needed to investigate more detailed risk factors and include mitigating factors of OS infection into the analysis. Full article

Background/Objectives: Our aim was to assess the feasibility of hyperspectral imaging (HSI) to detect changes in tissue oxygenation (StO₂) of the back, as non-invasive spinal cord collateral network (CN) monitoring during staged endovascular repair (ER) of thoracoabdominal aortic aneurysm (TAAA). Methods: Between September 2019 and June 2021, 20 patients were treated for TAAA and underwent HSI. They were randomized 1:1 to minimally invasive staged segmental artery coil embolization (MIS2ACE) (n = 10) and staged stentgraft implantation (n = 10) as priming methods. HSI of paravertebral regions was taken during each procedure and up to 10 days after. The primary endpoint was the identification of StO₂ changes after ER of TAAA. Results: TAAA Crawford Type II (n = 17) and Type III (n = 3) were treated. After stentgrafting, StO₂ increased immediately (p < 0.001), followed by a decrease after 5 days (p < 0.001) and 10 days (p = 0.028). StO₂ was significantly higher in the thoracic compared to the lumbar region. There was no significant difference between MIS2ACE and the first stentgrafting for StO₂ (p = 0.491). Following MIS2ACE, definitive ER caused a significant decrease in StO₂ after 5 days (p = 0.021), which recovered to baseline after 10 days (p = 0.130). After stentgraft priming, definitive ER caused a significant decrease in StO₂ after 24 h (p = 0.008), which did not return to baseline after 5 (p < 0.001) and 10 days (p = 0.019). Conclusions: HSI detected significant changes in StO₂ in the thoracic and lumbar paravertebral regions during ER of TAAA. These preliminary data suggest the efficacy of MIS2ACE in priming the CN before ER of TAAA. Full article

Background: Spinal cord injury (SCI) remains a profound medical challenge, with limited therapeutic options available. Studies focusing on individual molecular markers have limitations in addressing the complex disease process. Methods: This study utilizes RNA-sequencing (RNA-seq) to investigate the differentially expressed genes (DEGs) in spinal cord tissue from a rat SCI model at 1 and 21 days post-injury (dpi). After data processing and analysis, a series of biological pathway enrichment analyses were performed using online tools DAVID and GSEA. Interactions among the enriched genes were studied using Cytoscape software to visualize protein–protein interaction networks. Results: Our analysis identified 595 DEGs, with 399 genes significantly upregulated and 196 significantly downregulated at both time points. CD68 was the most upregulated gene at 21 dpi, with a significant fold change at 1 dpi. Conversely, MPZ was the most downregulated gene. Key immune response processes, including tumor necrosis factor (TNF) production, phagocytosis, and complement cascades, as well as systemic lupus erythematosus (SLE)-associated pathways, were enriched in the upregulated group. The enriched pathways in the downregulated group were related to the myelin sheath and neuronal synapse. Genes of interest from the most significantly downregulated DEGs were SCD, DHCR24, PRX, HHIP, and ZDHHC22. Upregulation of Fc-γ receptor genes, including FCGR2B and FCGR2A, points to potential autoimmune mechanisms. Conclusions: Our findings highlight complex immune and autoimmune responses that contribute to ongoing inflammation and tissue damage post-SCI, underscoring new avenues for therapeutic interventions targeting these molecular processes. Full article

Background: Scoliosis is a disorder characterized by an abnormal spinal curvature, which can lead to negative effects on patients, affecting their quality of life. Given its progressive nature, the classification of the scoliosis severity requires an accurate diagnosis and effective monitoring. The Cobb angle measurement method has been widely considered as the gold standard for a scoliosis assessment. Commonly, an expert assesses scoliosis severity manually by identifying the most tilted vertebrae of the spine. However, this method requires time, effort, and presents limitations in measurement accuracy, such as the intra- and inter-observer variability. Artificial intelligence provides more objective tools that are less sensitive to manual intervention aiming to transform the diagnosis of scoliosis. Objectives: The objective of this study was to address three key research questions regarding automated Cobb angle quantification: “Where is the spine in this radiograph?”, “What is its exact shape?”, and “Is the proposed method accurate?”. We propose the use of Mask R-CNN architecture for spine detection and segmentation in response to the first two questions, and a set of algorithms to tackle the third. Methods: The network’s detection and segmentation performance was evaluated through various metrics. An automated workflow for Cobb angle quantification and severity classification was developed. Finally, statistical methods provided the agreement between manual and automated measurements. Results: A high segmentation accuracy was achieved, highlighting the following: mIoU of 0.8012, and a mean precision of 0.9145. MAE was 2.96° ± 2.60° demonstrating a high agreement. Conclusions: The results obtained in this study demonstrate the potential of the proposed automated approach in clinical scenarios, which provides experts with a clear visualization of each stage in the scoliosis assessment by overlaying the results onto the X-ray image. Full article

The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning’s (ML’s) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning. Full article