Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network

Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise...

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Bibliographic Details
Published in:Journal of clinical medicine 2022-09, Vol.11 (18), p.5450
Main Authors: Trinh, Giam Minh, Shao, Hao-Chiang, Hsieh, Kevin Li-Chun, Lee, Ching-Yu, Liu, Hsiao-Wei, Lai, Chen-Wei, Chou, Sen-Yi, Tsai, Pei-I, Chen, Kuan-Jen, Chang, Fang-Chieh, Wu, Meng-Huang, Huang, Tsung-Jen
Format: Article
Language:English
Subjects:
Accuracy
Back pain
Classification
Efficiency
Machine learning
Magnetic resonance imaging
Medical research
Neural networks
Semantics
Vertebrae
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Summary:Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm11185450