The Success of Deep Learning Modalities in Evaluating Modic Changes

Abstract

BACKGROUND: Modic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities. METHODS: The sagittal T1, sagittal and axial T2 - weighted lumbar MRI images of 307 patients, of which 125 were female and 182 were male, aged 19 - 86 years, who underwent MRI examination between 2016 - 2021 were analyzed. Modic changes (MC) were categorized and marked according to signal changes. Our study consists of 2 independent stages: classification and segmentation. The categorized data were first classified using convolutional neural network (CNN) architectures such as DenseNet-121, DenseNet-169, and VGG-19. In the next stage, masks were removed by segmentation using U -Net, which is the CNN architecture, with image processing programs on the marked images. RESULTS: During the classification stage, the success rates for modic type 1, type 2, and type 3 changes were 98%, 96%, 100% in DenseNet-121, 100%, 94%, 100% in DenseNet-169, and 98%, 92%, 97% in VGG-19, respectively. At the segmentation phase, the success rate was 71% with the U -Net architecture. CONCLUSIONS: Evaluation of MRI findings of MC in the etiology of lower back pain with deep learning architec- tures can significantly reduce the workload of the radiol- ogist by providing ease of diagnosis.