Journal
JOURNAL OF CLINICAL MEDICINE
Volume 11, Issue 18, Pages -Publisher
MDPI
DOI: 10.3390/jcm11185450
Keywords
deep learning; LumbarNet; lumbar spine; spondylolisthesis; U-Net
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Funding
- Higher Education Sprout Project from the Ministry of Education (MOE) [DP2-110-21121-01-A-14]
- Industrial Technology Research Institute (ITRI) [L301ARE421]
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This study developed a computer-aided diagnostic algorithm, LumbarNet, to automatically detect spondylolisthesis. By analyzing complex structural patterns on lumbar X-ray images, LumbarNet outperformed the commonly used U-Net method and could reliably identify spondylolisthesis.
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.
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