The Role of Magnetic Resonance Imaging to Inform Clinical Decision-Making in Acute Spinal Cord Injury: A Systematic Review and Meta-Analysis

Validation and Feasibility of Ultrafast Cervical Spine MRI Using a Deep Learning-Assisted 3D Iterative Image Enhancement System

Purpose

This study aimed to evaluate the feasibility of ultrafast (2 min) cervical spine MRI protocol using a deep learning-assisted 3D iterative image enhancement (DL-3DIIE) system, compared to a conventional MRI protocol (6 min 14s).

Patients and Methods

Fifty-one patients were recruited and underwent cervical spine MRI using conventional and ultrafast protocols. A DL-3DIIE system was applied to the ultrafast protocol to compensate for the spatial resolution and signal-to-noise ratio (SNR) of images. Two radiologists independently assessed and graded the quality of images from the dimensions of artifacts, boundary sharpness, visibility of lesions and overall image quality. We recorded the presence or absence of different pathologies. Moreover, we examined the interchangeability of the two protocols by computing the 95% confidence interval of the individual equivalence index, and also evaluated the inter-protocol intra-observer agreement using Cohen's weighted kappa.

Results

Ultrafast-DL-3DIIE images were significantly better than conventional ones for artifacts and equivalent for other qualitative features. The number of cases with different kinds of pathologies was indistinguishable based on the MR images from ultrafast-DL-3DIIE and conventional protocols. With the exception of disc degeneration, the 95% confidence interval for the individual equivalence index across all variables did not surpass 5%, suggesting that the two protocols are interchangeable. The kappa values of these evaluations by the two radiologists ranged from 0.65 to 0.88, indicating good-to-excellent agreement.

Conclusion

The DL-3DIIE system enables 67% spine MRI scan time reduction while obtaining at least equivalent image quality and diagnostic results compared to the conventional protocol, suggesting its potential for clinical utility.

Comparison of machine learning algorithms for the classification of spinal cord tumor

Spinal cord Tumor has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into Bening or malignant has led many re- search teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, Logistic regression, Support Vector Machines (SVMs), Decision Trees (DTs), Random forest classifier(RFs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we have discussed a predictive model based on various supervised ML techniques.

A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.

Integrated Machine Learning Approach for the Early Prediction of Pressure Ulcers in Spinal Cord Injury Patients

(1) Background: Pressure ulcers (PUs) substantially impact the quality of life of spinal cord injury (SCI) patients and require prompt intervention. This study used machine learning (ML) techniques to develop advanced predictive models for the occurrence of PUs in patients with SCI. (2) Methods: By analyzing the medical records of 539 patients with SCI, we observed a 35% incidence of PUs during hospitalization. Our analysis included 139 variables, including baseline characteristics, neurological status (International Standards for Neurological Classification of Spinal Cord Injury [ISNCSCI]), functional ability (Korean version of the Modified Barthel Index [K-MBI] and Functional Independence Measure [FIM]), and laboratory data. We used a variety of ML methods-a graph neural network (GNN), a deep neural network (DNN), a linear support vector machine (SVM_linear), a support vector machine with radial basis function kernel (SVM_RBF), K-nearest neighbors (KNN), a random forest (RF), and logistic regression (LR)-focusing on an integrative analysis of laboratory, neurological, and functional data. (3) Results: The SVM_linear algorithm using these composite data showed superior predictive ability (area under the receiver operating characteristic curve (AUC) = 0.904, accuracy = 0.944), as demonstrated by a 5-fold cross-validation. The critical discriminators of PU development were identified based on limb functional status and laboratory markers of inflammation. External validation highlighted the challenges of model generalization and provided a direction for future research. (4) Conclusions: Our study highlights the importance of a comprehensive, multidimensional data approach for the effective prediction of PUs in patients with SCI, especially in the acute and subacute phases. The proposed ML models show potential for the early detection and prevention of PUs, thus contributing substantially to improving patient care in clinical settings.

Traumatic Spinal Cord Injury

OBJECTIVE

This article provides a review of the initial clinical and radiologic evaluation and treatment of patients with traumatic spinal cord injuries. It specifically highlights essential knowledge for neurologists who encounter patients with these complex injuries.

LATEST DEVELOPMENTS

There has been improvement in the care of patients with traumatic spinal cord injuries, particularly in the prehospital evaluation, approach for immediate immobilization, standardized spinal clearance, efficient triage, and transportation of appropriate patients to traumatic spinal cord injury specialized centers. Advancements in spinal instrumentation have improved the surgical management of spinal fractures and the ability to manage patients with spinal mechanical instability. The clinical evidence favors performing early surgical decompression and spine stabilization within 24 hours of traumatic spinal cord injuries, regardless of the severity or location of the injury. There is no evidence that supports the use of neuroprotective treatments to improve outcomes in patients with traumatic spinal cord injuries. The administration of high-dose methylprednisolone, which is associated with significant systemic adverse effects, is strongly discouraged. Early and delayed mortality rates continue to be high in patients with traumatic spinal cord injuries, and survivors often confront substantial long-term physical and functional impairments. Whereas the exploration of neuroregenerative approaches, such as stem cell transplantation, is underway, these methods remain largely investigational. Further research is still necessary to advance the functional recovery of patients with traumatic spinal cord injuries.

ESSENTIAL POINTS

Traumatic spinal cord injury is a complex and devastating condition that leads to long-term neurologic deficits with profound physical, social, and vocational implications, resulting in a diminished quality of life, particularly for severely affected patients. The initial management of traumatic spinal cord injuries demands comprehensive interdisciplinary care to address the potentially catastrophic multisystem effects. Ongoing endeavors are focused on optimizing and customizing initial management approaches and developing effective therapies for neuroprotection and neuroregeneration to enhance long-term functional recovery.

Management of Cervical Spinal Cord Injury without Major Bone Injury in Adults

The incidence of cervical spinal cord injury (CSCI) without major bone injury is increasing, possibly because older people typically have pre-existing cervical spinal canal stenosis. The demographics, neurological injury, treatment, and prognosis of this type of CSCI differ from those of CSCI with bone or central cord injury. Spine surgeons worldwide are debating on the optimal management of CSCI without major bone injury. Therefore, this narrative review aimed to address unresolved clinical questions related to CSCI without major bone injury and discuss treatment strategies based on current findings. The greatest divide among spine surgeons worldwide hinges on whether surgery is necessary for patients with CSCI without major bone injury. Certain studies have recommended early surgery within 24 h after injury; however, evidence regarding its superiority over conservative treatment remains limited. Delayed MRI may be beneficial; nevertheless, reliable factors and imaging findings that predict functional prognosis during the acute phase and ascertain the necessity of surgery should be identified to determine whether surgery/early surgery is better than conservative therapy/delayed surgery. Quality-of-life assessments, including neuropathic pain, spasticity, manual dexterity, and motor function, should be performed to examine the superiority of surgery/early surgery to conservative therapy/delayed surgery.

Reported adverse events during out-of-hospital mechanical ventilation and ventilatory support in emergency medical services and critical care transport crews: a systematic review

Background

Emergency medical services (EMS) and critical care transport crews constantly face critically-ill patients who need ventilatory support in scenarios where correct interventions can be the difference between life and death; furthermore, challenges like limited staff working on the patient and restricted spaces are often present. Due to these, mechanical ventilation (MV) can be a support by liberating staff from managing the airway and allowing them to focus on other areas; however, these patients face many complications that personnel must be aware of.

Aims

To establish the main complications related to out-of-hospital MV and ventilatory support through a systematic review.

Methodology

PubMed, BVS and Scopus were searched from inception to July 2021, following the PRISMA guidelines; search strategy and protocol were registered in PROSPERO. Two authors carried out an independent analysis of the articles; any disagreement was solved by mutual consensus, and data was extracted on a pre-determined spreadsheet. Only original articles were included, and risk of bias was assessed with quality assessment tools from the National Institutes of Health.

Results

The literature search yielded a total of 2,260 articles, of which 26 were included in the systematic review, with a total of 9,418 patients with out-of-hospital MV; 56.1% were male, and the age ranged from 18 to 82 years. In general terms of aetiology, 12.2% of ventilatory problems were traumatic in origin, and 64.8% were non-traumatic, with slight changes between out-of-hospital settings. Mechanical ventilation was performed 49.2% of the time in prehospital settings and 50.8% of the time in interfacility transport settings (IFTS). Invasive mechanical ventilation was used 98.8% of the time in IFTS while non-invasive ventilation was used 96.7% of the time in prehospital settings. Reporting of adverse events occurred in 9.1% of cases, of which 94.4% were critical events, mainly pneumothorax in 33.1% of cases and hypotension in 27.6% of cases, with important considerations between type of out-of-hospital setting and ventilatory mode; total mortality was 8.4%.

Conclusion

Reported adverse events of out-of-hospital mechanical ventilation vary between settings and ventilatory modes; this knowledge could aid EMS providers in promptly recognizing and resolving such clinical situations, depending on the type of scenario being faced.

The Role of Magnetic Resonance Imaging and Computed Tomography in Spinal Cord Injury

Traumatic injuries of the spine are associated with long-term morbidity and mortality. Timely diagnosis and appropriate management of mechanical instability and spinal cord injury are important to prevent further neurologic deterioration. Spine surgeons require an understanding of the essential imaging techniques concerning the diagnosis, management, and prognosis of spinal cord injury. We present a review in the role of computed tomography (CT) including advancements in multidetector CT (MDCT), dual energy CT (DECT), and photon counting CT, and how it relates to spinal trauma. We also review magnetic resonance imaging (MRI) and some of the developed MRI based classifications for prognosticating the severity and outcome of spinal cord injury, such as diffusion weighted imaging (DWI), diffusion tractography (DTI), functional MRI (fMRI), and perfusion MRI.

Prognostic MRI parameters in acute traumatic cervical spinal cord injury

PURPOSE

The aim of this study is to estimate the prognostic value of some features documented on preoperative MRI study in patients with acute cervical spinal cord injury.

METHODS

The study was conducted in patients operated for cervical spinal cord injury (cSCI) from April 2014 to October 2020. The quantitative analysis on preoperative MRI scans included: length of the spinal cord intramedullary lesion (IMLL the canal diameter at the level of maximal spinal cord compression (MSCC) and the presence of intramedullary hemorrhage. The canal diameter at the MSCC was measured on the middle sagittal FSE-T2W images at the maximum level of injury. The America Spinal Injury Association (ASIA) motor score was used for neurological assessment at hospital admission. At 12-month follow-up all patients were examined with the SCIM questionnaire.

RESULTS

At linear regression analysis, the length of the spinal cord lesion [β coefficient -10.35, 95% confidence interval (CI)-13.71 to-6.99; p < 0.001], the diameter of the canal at the level of the MSCC (β coefficient 6.99, 95% CI 0.65 to 13.33; p = 0.032), and the intramedullary hemorrhage (β coefficient  - 20.76, 95% CI - 38.70 to - 2.82; p = 0.025), were significantly associated with the score at the SCIM questionnaire at one year follow-up: shorter spinal cord lesion, greater diameter of the canal at the level of the MSCC, and absence of intramedullary hemorrhage were predictors of better outcome.

CONCLUSION

According to the findings of our study, the spinal length lesion, canal diameter at the level of spinal cord compression and intramedullary hematoma documented by the preoperative MRI study were associated with the prognosis of patients with cSCI.

Effectiveness of different surgical methods in the treatment of acute central cord syndrome without fractures and dislocations of the cervical spine

BACKGROUND

Acute central cord syndrome (ACCS) without fractures or dislocations is the most common form of incomplete spinal cord injury.

OBJECTIVE

To evaluate the effectiveness of different surgical methods in the treatment of acute central cord syndrome without fractures or dislocations of the cervical spine.

METHODS

A total of 164 patients with ACCS without fracture or dislocation of the cervical spine treated in our hospital from May 2012 to October 2019 were recruited and assigned to study group A and study group B according to different treatment modalities, with 82 cases in each group. Study group A underwent anterior cervical discectomy and fusion, and study group B was treated with posterior cervical laminectomy. The American Spinal Injury Association (ASIA) classification and motor scores of all cases at admission and at discharge were recorded, and the treatment outcomes of the two groups were compared.

RESULTS

No significant differences were found in the ASIA classification and ASIA motor scores between the two groups at admission (P> 0.05). One year after surgery, the ASIA motor scores and sensory scores were not statistically significant between the two groups (P> 0.05) but showed significant improvement compared to the preoperative scores (P< 0.05).

CONCLUSION

Both anterior cervical discectomy and fusion and posterior cervical laminectomy can improve the ASIA classification, ASIA motor scores, and sensory scores of ACCS patients without fractures or dislocations of the cervical spine. Therefore, surgical methods should be adopted based on the patients' conditions.

Ultrafast cervcial spine MRI protocol using deep learning-based reconstruction: Diagnostic equivalence to a conventional protocol

PURPOSE

A major drawback of magnetic resonance imaging (MRI) is its limited imaging speed. This study proposed an ultrafast cervical spine MRI protocol (2 min 57 s) using deep learning-based reconstruction (DLR) and compared the diagnostic results to those of conventional MRI protocols (12 min 54 s).

METHODS

Fifty patients who underwent cervical spine MRI using both conventional and ultrafast protocols, including sagittal T1-weighted, T2-weighted, short-TI inversion recovery, and axial T2*-weighted imaging were included in this study. The ultrafast protocol shortened the acquisition time to approximately-one-fourth of that of the conventional protocol by reducing the phase matrix, oversampling rate, and number of excitations, and by applying compressed sensing. To compensate for the decreased signal-to-noise ratio caused by acceleration, noise reduction using DLR was performed. For image interpretation, three neuroradiologists graded or classified degenerative changes, including central canal stenosis, foraminal stenosis, endplate degeneration, disc degeneration, and disc hernia. The presence of other pathologies was also recorded. Given the absence of a reference standard, we tested the interchangeability of the two protocols by calculating the 95% confidence interval (CI) of the individual equivalence index. We also assessed the inter-protocol intra-reader agreement using kappa statistics.

RESULTS

Except for endplate degeneration, the 95 % CI of the individual equivalence index for all variables did not exceed 5 %, indicating interchangeability between the two protocols. The kappa values ranged from 0.600 to 0.977, indicating substantial to almost perfect agreement.

CONCLUSIONS

The proposed ultrafast MRI protocol yielded almost equivalent diagnostic results compared as the conventional protocol.