Use of machine learning to select texture features in investigating the effects of axial loading on T2-maps from magnetic resonance imaging of the lumbar discs.

BACKGROUND: Recent advances in texture analysis and machine learning offer new opportunities to improve the application of imaging to intervertebral disc biomechanics. This study employed texture analysis and machine learning on MRIs to investigate the lumbar disc's response to loading. METHODS: Thirty-five volunteers (30 (SD 11) yrs.) with and without chronic back pain spent 20 min lying in a relaxed unloaded supine position, followed by 20 min loaded in compression, and then 20 min with traction applied. T2-weighted MR images were acquired during the last 5 min of each loading condition. Custom image analysis software was used to segment discs from adjacent tissues semi-automatically and segment each disc into the nucleus, anterior and posterior annulus automatically. A grey-level, co-occurrence matrix with one to four pixels offset in four directions (0°, 45°, 90° and 135°) was then constructed (320 feature/tissue). The Random Forest Algorithm was used to select the most promising classifiers. Linear mixed-effect models and Cohen's d compared loading conditions. FINDINGS: All statistically significant differences (p < 0.001) were observed in the nucleus and posterior annulus in the 135° offset direction at the L4-5 level between lumbar compression and traction. Correlation (P2-Offset, P4-Offset) and information measure of correlation 1 (P3-Offset, P4-Offset) detected significant changes in the nucleus. Statistically significant changes were also observed for homogeneity (P2-Offset, P3-Offset), contrast (P2-Offset), and difference variance (P4-Offset) of the posterior annulus. INTERPRETATION: MRI textural features may have the potential of identifying the disc's response to loading, particularly in the nucleus and posterior annulus, which appear most sensitive to loading. LEVEL OF EVIDENCE: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.

Overcrowding in emergency departments: an overview of reviews describing global solutions and their outcomes.

Emergency Department (ED) crowding is defined as a situation wherein the demands of emergency services overcome the ability of a department to provide high-quality care within an appropriate time frame. There is a need for solutions, as the harms of crowding impact patients, staff, and healthcare spending. An overview of ED crowding was previously published by our group, which outlines these global issues. The problem of overcrowding in emergency departments has emerged as a global public health concern, and several healthcare agencies have addressed the issue and proposed possible solutions at each level of emergency care. There is no current literature summarizing the extensive research on interventions and solutions, thus there is a need for data synthesis to inform policymakers in this field. The aim of this overview was to summarize the interventions at each level of emergency care: input, throughput, and output. The methodology was supported by the current PRIOR statement for an overview of reviews. The study summarized twenty-seven full-text systematic reviews, which encompassed three hundred and eight primary studies. The results of the summary displayed a requirement for increasing studies in input and output interventions, as these showed the best outcomes with regard to ED crowding metrics. Moreover, the results displayed heterogeneous results at each level of ED care; these reflected that generally solutions have not been matched to specific problems facing regional centres. Thus, individual factors need to be considered when implementing solutions in Emergency Departments.

Texture analysis in the classification of T2 -weighted magnetic resonance images in persons with and without low back pain.

Magnetic resonance imaging findings often do not distinguish between people with and without low back pain (LBP). However, there are still a large number of people who undergo magnetic resonance imaging to help determine the etiology of their back pain. Texture analysis shows promise for the classification of tissues that look similar, and machine learning can minimize the number of comparisons. This study aimed to determine if texture features from lumbar spine magnetic resonance imaging differ between people with and without LBP. In total, 14 participants with chronic LBP were matched for age, weight, and gender with 14 healthy volunteers. A custom texture analysis software was used to construct a gray-level co-occurrence matrix with one to four pixels offset in 0° direction for the disc and superior and inferior endplate regions. The Random Forests Algorithm was used to select the most promising classifiers. The linear mixed-effect model analysis was used to compare groups (pain vs. pain-free) at each level controlling for age. The Random Forest Algorithm recommended focusing on intervertebral discs and endplate zones at L4-5 and L5-S1. Differences were observed between groups for L5-S1 superior endplate contrast, homogeneity, and energy (p = .02). Differences were observed for L5-S1 disc contrast and homogeneity (p < .01), as well as for the inferior endplates contrast, homogeneity, and energy (p < .03). Magnetic resonance imaging textural features may have potential in identifying structures that may be the target of further investigations about the reasons for LBP.