Classification and Radiological Diagnosis of Thoracolumbar Spine Fractures: WFNS Spine Committee Recommendations

Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A systematic review and meta-analysis

Introduction

Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivating the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite varying evidence, the noteworthy transformative potential of AI in healthcare, leveraging insights from daily healthcare data, persists.

Research question

This review investigates the utilization of ML and DL in TLIs causing VFs.

Materials and methods

Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in PubMed and Scopus databases, identifying 793 studies. Seventeen were included in the systematic review, and 11 in the meta-analysis. Variables considered encompassed publication years, geographical location, study design, total participants (14,524), gender distribution, ML or DL methods, specific pathology, diagnostic modality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical summary receiver operating characteristic curve (HSROC) analysis.

Results

Predominantly conducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.068-0.137).

Conclusion

The study underscores consistent specificity and sensitivity estimates, affirming the diagnostic test's robustness. However, the broader context of ML applications in TLIs emphasizes the critical need for standardization in methodologies to enhance clinical utility.

Successful implementation of prophylactic veno-venoarterial extracorporeal membrane oxygenation in high-risk trauma surgery: A case report

INTRODUCTION

Extracorporeal Membrane Oxygenation (ECMO) is increasingly utilized in trauma care, yet its elective use during high-risk surgeries remains unreported.

CASE REPORT

We report a successful instance of prophylactic ECMO support via a Veno-Venoarterial (V-VA) configuration during high-risk surgery in a patient with extensive trauma, including severe thoracic damage and a highly unstable thoracic spine fracture. V-VA ECMO prevented complications such as hemodynamic and respiratory collapse associated with chest compression during the surgical procedure, as the patient should be in a prone position.

DISCUSSION

The potential of ECMO as prophylactic support in high-risk surgery amongst trauma patients underscores a novel application of this technology. Complex configurations must be evaluated to avoid associated ECMO complications.

CONCLUSION

Our case highlights the potential of prophylactic ECMO hybrid modes, indicating their safe application during high-risk procedures in select trauma patients.

Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography

BACKGROUND

Acute vertebral fracture is usually caused by low-energy injury with osteoporosis and high-energy trauma. The AOSpine thoracolumbar spine injury classification system (AO classification) plays an important role in the diagnosis and treatment of the disease. The diagnosis and description of vertebral fractures according to the classification scheme requires a great deal of time and energy for radiologists.

PURPOSE

To design and validate a multistage deep learning system (multistage AO system) for the automatic detection, localization and classification of acute thoracolumbar vertebral body fractures according to AO classification on computed tomography.

MATERIALS AND METHODS

The CT images of 1,217 patients who came to our hospital from January 2015 to December 2019 were collected retrospectively. The fractures were marked and classified by 2 junior radiology residents according to the type A standard in the AO classification. Marked fracture sites included the upper endplate, lower endplate and posterior wall. When there were inconsistent opinions on classification labels, the final result was determined by a director radiologist. We integrated different networks into different stages of the overall framework. U-net and a graph convolutional neural network (U-GCN) are used to realize the location and classification of the thoracolumbar spine. Next, a classification network is used to detect whether the thoracolumbar spine has a fracture. In the third stage, we detect fractures in different parts of the thoracolumbar spine by using a multibranch output network and finally obtain the AO types.

RESULTS

The mean age of the patients was 61.87 years with a standard deviation of 17.04 years, consisting of 760 female patients and 457 male patients. On vertebrae level, sensitivity for fracture detection was 95.23% in test dataset, with an accuracy of 97.93% and a specificity of 98.35%. For the classification of vertebral body fractures, the balanced accuracy was 79.56%, with an AUC of 0.904 for type A1, 0.945 for type A2, 0.878 for type A3 and 0.942 for type A4.

CONCLUSION

The multistage AO system can automatically detect and classify acute vertebral body fractures in the thoracolumbar spine on CT images according to AO classification with high accuracy.

A survey on the early management of spinal trauma in low and middle-income countries: From the scene of injury to the diagnostic phase (part II)

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Most spinal trauma worldwide occurs in low-and middle-income countries (LMICs). Several factors may limit the applicability of current guidelines as regards the early management of spinal injury.

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The pre-hospital management per se of spinal trauma in LMICs is subject to partial adherence to recommendations, with possible impact on patient outcomes.

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The use of clinical (eg ASIA) and morphological (eg SLIC, TLICS, AO Spine) grading scales is not homogeneous.

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The availability and cost of diagnostic equipment, and the timing of emergency imaging can vary significantly from one region to another, probably affecting the timely management of spinal injury patients.

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The introduction of resource-targeted guidelines for spinal trauma may be a valuable option to overcome the limitations of real-life application of current guidelines.