Lumbar Spinal Stenosis Grading in Multiple Level Magnetic Resonance Imaging Using Deep Convolutional Neural Networks.

ABSTRACT: Study DesignRetrospective magnetic resonance imaging grading with comparison between experts and deep convolutional neural networks (CNNs).ObjectiveThe application of deep learning to clinical diagnosis has gained popularity. This approach can accelerate image interpretation and serve as a screening tool to help doctors.MethodsA comparison was conducted between retrospective magnetic resonance imaging (MRI) grading performed by experts and grading obtained using CNN classifiers. Data were collected from the lumbar axial dataset in the DICOM format. Two experts labeled the sampled images using the same diagnostic tools: localization of patches near the spinal canal, rootlet leveling, and stenosis grading. Comprehensive comparisons were presented for both rootlet cord classification and sten

SUBMITTER: Won D 

PROVIDER: S-EPMC11559735 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature