Feasibility and safety report on robotic assistance for cervical pedicle screw fixation: a cadaveric study.

This cadaveric study aimed to evaluate the safety and usability of a novel robotic system for posterior cervical pedicle screw fixation. Three human cadaveric specimens and C2-T3 were included. Freshly frozen human cadaver specimens were prepared and subjected to robot-assisted posterior cervical pedicle screw fixation using the robotic system. The accuracy of screw placement, breach rate, and critical structure violations were evaluated. The results were statistically compared with those of previous studies that used different robotic systems for cervical pedicle screw fixation. The robotic system demonstrated a high accuracy rate in screw placement. A significant number of screws were placed within predetermined safe zones. The total entry offset was 1.08 ± 0.83 mm, the target offset was 1.86 ± 0.50 mm, and the angle offset was 2.14 ± 0.77°. Accuracy rates comparable with those of previous studies using different robotic systems were achieved. The system was also feasible, allowing precise navigation and real-time feedback during the procedure. This cadaveric study validated the safety and usability of the novel robotic system for posterior cervical pedicle screw fixation. The system exhibited high precision in screw placement, and the results support the extension of the indications for robot-assisted pedicle screw fixation from the lumbar spine to the cervical spine.

Whole Spine Segmentation Using Object Detection and Semantic Segmentation.

OBJECTIVE: Virtual and augmented reality have enjoyed increased attention in spine surgery. Preoperative planning, pedicle screw placement, and surgical training are among the most studied use cases. Identifying osseous structures is a key aspect of navigating a 3-dimensional virtual reconstruction. To automate the otherwise time-consuming process of labeling vertebrae on each slice individually, we propose a fully automated pipeline that automates segmentation on computed tomography (CT) and which can form the basis for further virtual or augmented reality application and radiomic analysis. METHODS: Based on a large public dataset of annotated vertebral CT scans, we first trained a YOLOv8m (You-Only-Look-Once algorithm, Version 8 and size medium) to detect each vertebra individually. On the then cropped images, a 2D-U-Net was developed and externally validated on 2 different public datasets. RESULTS: Two hundred fourteen CT scans (cervical, thoracic, or lumbar spine) were used for model training, and 40 scans were used for external validation. Vertebra recognition achieved a mAP50 (mean average precision with Jaccard threshold of 0.5) of over 0.84, and the segmentation algorithm attained a mean Dice score of 0.75 ± 0.14 at internal, 0.77 ± 0.12 and 0.82 ± 0.14 at external validation, respectively. CONCLUSION: We propose a 2-stage approach consisting of single vertebra labeling by an object detection algorithm followed by semantic segmentation. In our externally validated pilot study, we demonstrate robust performance for our object detection network in identifying individual vertebrae, as well as for our segmentation model in precisely delineating the bony structures.

TomoRay: Generating Synthetic Computed Tomography of the Spine From Biplanar Radiographs.

OBJECTIVE: Computed tomography (CT) imaging is a cornerstone in the assessment of patients with spinal trauma and in the planning of spinal interventions. However, CT studies are associated with logistical problems, acquisition costs, and radiation exposure. In this proof-of-concept study, the feasibility of generating synthetic spinal CT images using biplanar radiographs was explored. This could expand the potential applications of x-ray machines pre-, post-, and even intraoperatively. METHODS: A cohort of 209 patients who underwent spinal CT imaging from the VerSe2020 dataset was used to train the algorithm. The model was subsequently evaluated using an internal and external validation set containing 55 from the VerSe2020 dataset and a subset of 56 images from the CTSpine1K dataset, respectively. Digitally reconstructed radiographs served as input for training and evaluation of the 2-dimensional (2D)-to-3-dimentional (3D) generative adversarial model. Model performance was assessed using peak signal to noise ratio (PSNR), structural similarity index (SSIM), and cosine similarity (CS). RESULTS: At external validation, the developed model achieved a PSNR of 21.139 ± 1.018 dB (mean ± standard deviation). The SSIM and CS amounted to 0.947 ± 0.010 and 0.671 ± 0.691, respectively. CONCLUSION: Generating an artificial 3D output from 2D imaging is challenging, especially for spinal imaging, where x-rays are known to deliver insufficient information frequently. Although the synthetic CT scans derived from our model do not perfectly match their ground truth CT, our proof-of-concept study warrants further exploration of the potential of this technology.

Risk of insomnia symptoms according to Work-Family Conflict by workers' characteristics.

Background: Work-Family Conflict means that the demands of work and family roles cannot be met simultaneously, so one cannot concentrate on one's work or family role. This conflict can negatively affect mental health and cause insomnia symptoms. Methods: This study was conducted on 20,442 subjects. Insomnia symptoms were assessed using the Minimal Insomnia Symptom Scale, and other variables were assessed using the questionnaire method. Logistic regression analyses were performed to evaluate the effect of Work-Family Conflict on insomnia symptoms, and subgroup logistic regression analyses were also performed. Results: The number of people with insomnia symptoms was 4,322 (15.1%). Compared with Low Work-Family Conflict, the odds ratios (ORs) for the risk of insomnia symptoms were 1.84 (95% confidence interval: 1.56-2.16) in High work-to-family conflict, 1.16 (1.02-1.32) in High family-to-work conflict, and 3.19 (2.87-3.55) in High Work-Family Conflict. The ORs were higher for men than women in High WFC but higher for women than men in High Work-Family Conflict. Conclusions: The risk of insomnia symptoms was highest in High Work-Family Conflict.

Long-Term Outcomes after Multimodal Treatment for Clival Chordoma: Efficacy of the Endonasal Transclival Approach with Early Adjuvant Radiation Therapy.

This study investigates the long-term outcomes of clival chordoma patients treated with the endonasal transclival approach (ETCA) and early adjuvant radiation therapy. A retrospective review of 17 patients (2002-2013) showed a 10-year progression-free survival (PFS) rate of 67.4%, with the ETCA group showing fewer progressions and cranial neuropathies than those treated with combined approaches. The ETCA, a minimally invasive technique, provided a similar extent of resection compared to conventional skull-base approaches and enabled safe delivery of high-dose adjuvant radiotherapy. The findings suggest that ETCA is an effective treatment for centrally located clival chordomas.

Long-term results comparison after anterior cervical discectomy with BGS-7 spacer (NOVOMAX®-C) and allograft spacer: A prospective observational study.

Introduction: In an anterior cervical discectomy and fusion (ACDF), various types of graft materials including autograft, allograft, and synthetic graft have been used to achieve adequate spinal fusion. Allograft spacer is mainly used in cervical fusion, especially in the anterior approach. The synthetic bone graft material BGS-7(CaO-SiO2-P2O5-B2O3, bioactive Glass-Ceramics) can bind with surrounding bone tissue by forming a hydroxyapatite layer bone bridge, leading to faster graft osseointegration. This study was conducted to compare long-term clinical outcome of BGS-7 spacer and allograft spacer for anterior cervical discectomy and fusion surgery. Materials and Methods: From September 2014 to December 2016, Consecutive anterior cervical discectomy and fusion surgeries using a BGS-7 spacer (N = 18) and Allograft spacer (N = 26) were compared for postoperative clinical outcomes. Radiologic assessments were performed, and Instrumental failure, including breakage, cage migration, subsidence were observed and Fusion status were analyzed. Finite element analysis was performed for simulating mechanical stress between the vertebral body and implant. Clinical outcomes were evaluated using neck VAS, NDI, and JOA on the patient's final follow-up visits. Results: Among the 44 patients who underwent an anterior cervical discectomy and fusion surgery using the BGS-7 spacer and Allograft spacer, there were 30 men and 14 women. The average age at the operation was 47.69 ± 10.49 in allograft spacer and 51.67 ± 11.03 in BGS-7 spacer. The mean follow-up period was 89.18 ± 5.44 months. Twenty three (88.46%) patients in allograft spacer and 20(100%) patients in BGS-7 spacer were demonstrated radiologic evidence of interbody fusion in last OPD, which accounts for fusion grade 4 or 5. Peak stresses were 343.85 MPa in allograft spacer, and 132.55 MPa in BGS-7 spacer. Long-term clinical outcomes including neck VAS, NDI, and JOA didn't show statistical differences between the two groups. There were no adverse events related to the BGS-7 spacer.10.3389/fbioe.2023.110046. Conclusion: The BGS-7 spacer demonstrated reliability as a spacer in anterior cervical discectomy and fusionF surgery without instrumental failure. Early stabilization with a bony bridge formation was observed at the intermediate follow-up period, and the long-term clinical outcome was favorable at more than 60 months after surgery without any adverse events. Thus, the BGS-7 spacer is a safe and effective alternative to the allograft spacer in anterior cervical discectomy and fusion surgery.

Risk factors for unplanned reoperation after corrective surgery for adult spinal deformity.

To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles.

Clozapine-Induced Chemogenetic Neuromodulation Rescues Post-Stroke Deficits After Chronic Capsular Infarct.

Long-term disabilities induced by stroke impose a heavy burden on patients, families, caregivers, and public health systems. Extensive studies have demonstrated the therapeutic value of neuromodulation in enhancing post-stroke recovery. Among them, chemogenetic neuromodulation activated by clozapine-N-oxide (CNO) has been proposed as the potential tool of neuromodulation. However, recent evidence showed that CNO does not cross the blood - brain barrier and may in fact have low binding affinity for chemogenetic tool. Thus, clozapine (CLZ) has been suggested for use in chemogenetic neuromodulation, in place of CNO, because it readily crosses the blood-brain barrier. Previously we reported that low doses of CLZ (0.1 mg/kg) successfully induced neural responses without off-target effects. Here, we show that low-dose clozapine (0.1 mg/kg) can induce prolonged chemogenetic activation while avoiding permeability issues and minimizing off-target effects. In addition, clozapine-induced excitatory chemogenetic neuromodulation (CLZ-ChemoNM) of sensory-parietal cortex with hsyn-hM3Dq-YFP-enhanced motor recovery in a chronic capsular infarct model of stroke in rats, improving post-stroke behavioral scores to 56% of pre-infarct levels. Longitudinal 2-deoxy-2-[18F]-fluoro-D-glucose microPET (FDG-microPET) scans showed that a reduction in diaschisis volume and activation of corticostriatal circuits were both correlated with post-stroke recovery. We also found c-Fos increases in bilateral cortices and BDNF increases in the cortices and striatum after CLZ-ChemoNM, indicating an increase in neural plasticity. These findings suggest the translational feasibility of CLZ-ChemoNM for augmenting recovery in chronic stroke.

Clinical Effect of Transverse Process Hook with K-Means Clustering-Based Stratification of Computed Tomography Hounsfield Unit at Upper Instrumented Vertebra Level in Adult Spinal Deformity Patients.

OBJECTIVE: This study aimed to investigate the efficacy of transverse process (TP) hook system at the upper instrumented vertebra (UIV) for preventing screw pullout in adult spinal deformity surgery using the pedicle Hounsfield unit (HU) stratification based on K-means clustering. METHODS: We retrospectively reviewed 74 patients who underwent deformity correction surgery between 2011 and 2020 and were followed up for >12 months. Pre- and post-operative data were used to determine the incidence of screw pullout, UIV TP hook implementation, vertebral body HU, pedicle HU, and patient outcomes. Data was then statistically analyzed for assessment of efficacy and risk prediction using stratified HU at UIV level alongside the effect of the TP hook system. RESULTS: The screw pullout rate was 36.4% (27/74). Perioperative radiographic parameters were not significantly different between the pullout and non-pullout groups. The vertebral body HU and pedicle HU were significantly lower in the pullout group. K-means clustering stratified the vertebral body HU ≥205.3, <137.2, and pedicle HU ≥243.43, <156.03. The pullout rate significantly decreases in patients receiving the hook system when the pedicle HU was from ≥156.03 to < 243.43 (p<0.05), but the difference was not statistically significant in the vertebra HU stratified groups and when pedicle HU was ≥243.43 or <156.03. The postoperative clinical outcomes improved significantly with the implementation of the hook system. CONCLUSION: The UIV hook provides better clinical outcomes and can be considered a preventative strategy for screw-pullout in the certain pedicle HU range.

The Combined Effects of RhBMP-2 and Systemic RANKL Inhibitor in Patients With Bone Density Loss Undergoing Posterior Lumbar Interbody Fusion: A Retrospective Observational Analysis With Propensity Score Matching.

OBJECTIVE: The risks of nonunion and subsidence are high in patients with bone density loss undergoing spinal fusion surgery. The internal application of recombinant human bone morphogenic protein 2 (rhBMP-2) in an interbody cage improves spinal fusion; however, related complications have been reported. Denosumab, a human monoclonal antibody targeting the receptor activator of nuclear factor kappa B ligand (RANKL), hinders osteoblast differentiation and function. Therefore, this study aimed to observe the combined effect of the local application of rhBMP-2 in a lumbar cage and systemic RANKL inhibition on postoperative spinal fusion in patients with bone density loss undergoing posterior lumbar interbody fusion (PLIF). METHODS: This retrospective observational study included 251 consecutive patients with spinal stenosis who underwent PLIF at a single center between 2017 and 2021. Clinical outcomes were assessed, and radiographic evaluations included lumbar flexion, extension, range of motion, and subsidence. Statistical analyses were conducted to identify the combined effect of the treatment and the subsidence and spinal fusion status. RESULTS: One hundred patients were included in the final analysis. Denosumab treatment significantly reduced the rate of osteolysis (p = 0.013). When denosumab was administered in combination with rhBMP-2, the fusion status remained similar; however, the incidences of postoperative osteolysis and postoperative oozing day decreased. CONCLUSION: The combined use of rhBMP-2 and RANKL inhibition in patients with bone density loss can enhance bone formation after PLIF with fewer complications than rhBMP-2 alone.

Pilot study of a single-channel EEG seizure detection algorithm using machine learning.

OBJECTIVE: Seizures are one of the most common emergencies in the neonatal intensive care unit (NICU). They are identified through visual inspection of electroencephalography (EEG) reports and treated by neurophysiologic experts. To support clinical seizure detection, several feature-based automatic neonatal seizure detection algorithms have been proposed. However, as they were unsuitable for clinical application due to their low accuracy, we developed a new seizure detection algorithm using machine learning for single-channel EEG to overcome these limitations. METHODS: The dataset applied in our algorithm contains EEG recordings from human neonates. A 19-channel EEG system recorded the brain waves of 79 term neonates admitted to the NICU at the Helsinki University Hospital. From these datasets, we selected six patients with conformational seizure annotations for the pilot study and allocated four and two patients for our training and testing datasets, respectively. The presence of seizures in the EEGs was annotated independently by three experts through visual interpretation. We divided the data into epochs of 5 s each and further defined a seizure block to label the annotations from each expert recorded every second. Subsequently, to create a balanced dataset, any data point with a non-seizure label was moved to the training and test dataset. RESULT: The developed principal component feature-extracted machine learning algorithm used 62.5% of the relative time (only 5 s for decision) of the baseline, reaching an area under the ROC curve score of 0.91. The effect of diversified parameters was meticulously examined, and 100 principal components were extracted to optimize the model performance. CONCLUSION: Our machine learning-based seizure detection algorithm exhibited the potential for clinical application in NICUs, general wards, and at home and proved its convenience by requiring only a single channel for implementation.

Optimizing clozapine for chemogenetic neuromodulation of somatosensory cortex.

Clozapine (CLZ) has been proposed as an agonist for Designer Receptors Exclusively Activated by Designer Drugs (DREADDs), to replace Clozapine-N-oxide (CNO); however, there are no reliable guidelines for the use of CLZ for chemogenetic neuromodulation. We titrated the optimal dose of CLZ required to evoke changes in neural activity whilst avoiding off-target effects. We also performed [18F]Fluoro-deoxy-glucose micro positron emission tomography (FDG-microPET) scans to determine the global effect of CLZ-induced hM3D(Gq) DREADD activation in the rat brain. Our results show that low doses of CLZ (0.1 and 0.01 mg/kg) successfully induced neural responses without off-target effects. CLZ at 1 mg/kg evoked a stronger and longer-lasting neural response but produced off-target effects, observed as changes in locomotor behavior and FDG-microPET imaging. Unexpectedly, FDG-microPET imaging failed to demonstrate an increase in regional glucose metabolism in the stimulated cortex during CLZ chemogenetic neuromodulation. Therefore, caution should be used when interpreting FDG-PET images in the context of cortical chemogenetic activation.

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R2 value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.

Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state. MATERIALS AND METHODS: We used two rs-fMRI cohorts: the public Alzheimer's disease Neuroimaging Initiative database (ADNI2) and an in-house Alzheimer's disease cohort from South Korea, both including individuals with AD, MCI, and normal controls. After extracting three-dimensional (3-D) patterns measuring regional coherence and functional connectivity during the resting state, we performed univariate statistical t-tests to generate a 3-D mask that retained only voxels showing significant changes. Given the initial univariate features, to enhance discriminative patterns, we implemented MVPA feature reduction using support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), in combination with the univariate t-test. Classifications were performed by an ELM, and its efficiency was compared to linear and nonlinear (radial basis function) SVMs. RESULTS: The maximal accuracies achieved by the method in the ADNI2 cohort were 98.86% (p<0.001) and 98.57% (p<0.001) for AD and MCI vs. CN, respectively. In the in-house cohort, the same accuracies were 98.70% (p<0.001) and 94.16% (p<0.001). CONCLUSION: From a clinical perspective, combining extreme learning machine and hybrid MVPA applied on concatenations of multiple rs-fMRI biomarkers can potentially assist the clinicians in AD and MCI diagnosis.

Evaluation of Functional Decline in Alzheimer's Dementia Using 3D Deep Learning and Group ICA for rs-fMRI Measurements.

Purpose: To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data. Method: We divided 133 Alzheimer's disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5-1) and moderate to severe (CDR: 2-3) dementia consisted of 77 and 56 subjects, respectively. We used rs-fMRI to extract functional connectivity features, calculated using independent component analysis (ICA), and performed automated severity classification with three-dimensional convolutional neural networks (3D-CNNs) based on deep learning. Results: The mean balanced classification accuracy was 0.923 ± 0.042 (p < 0.001) with a specificity of 0.946 ± 0.019 and sensitivity of 0.896 ± 0.077. The rs-fMRI data indicated that the medial frontal, sensorimotor, executive control, dorsal attention, and visual related networks mainly correlated with dementia severity. Conclusions: Our CDR-based novel classification using rs-fMRI is an acceptable objective severity indicator. In the absence of trained neuropsychologists, dementia severity can be objectively and accurately classified using a 3D-deep learning framework with rs-fMRI independent components.

Mild Cognitive Impairment Diagnosis Using Extreme Learning Machine Combined With Multivoxel Pattern Analysis on Multi-Biomarker Resting-State FMRI.

This paper proposed a classification framework that integrates hybrid multivoxel pattern analyses (MVPA) and extreme learning machine (ELM) for automated Mild Cognitive Impairment (MCI) diagnosis applied on concatenations of multi-biomarker resting-state fMRI. Given three-dimensional (3D) regional coherences and functional connectivity patterns measured during resting state, we performed 3D univariate t-tests to obtain initial univariate features which show the significant changes. To enhance discriminative patterns, we employed multivariate feature reductions using recursive feature elimination in combination with univariate t-test. The maximal amount of information changes were achieved by concatenations of multiple functional metrics. The classifications were performed by an ELM, and its efficiency was compared to SVMs. This study reported mean accuracies using 10-fold cross-validation, followed by permutation tests to assess the statistical significance of discriminative results. In diagnosis of MCI, the proposed method achieved a maximal accuracy of 97.86% (p<; 0.001) in ADNI2 cohort and thus has potentials to assist the clinicians in MCI diagnosis.