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Texture analysis in the classification of T 2 ‐weighted magnetic resonance images in persons with and without low back pain
Author(s) -
Abdollah Vahid,
Parent Eric C.,
Dolatabadi Samin,
Marr Erica,
Croutze Roger,
Wachowicz Keith,
Kawchuk Greg
Publication year - 2021
Publication title -
journal of orthopaedic research®
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.041
H-Index - 155
eISSN - 1554-527X
pISSN - 0736-0266
DOI - 10.1002/jor.24930
Subject(s) - magnetic resonance imaging , medicine , low back pain , lumbar , contrast (vision) , radiology , nuclear medicine , artificial intelligence , computer science , pathology , alternative medicine
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.