AI-Enabled Diagnosis of Lumbar Spinal Stenosis from Axial MR Images Using Convolutional Neural Network and Image Explainer

Lower back pain (LBP) is a global medical condition that affects more than $\mathbf{8 0 \%}$ of people at least once in their lifetime. Various abnormal conditions could cause LBP. One of these conditions is Lumbar Spinal Stenosis (LSS), which is usually a serious condition, hence the need for its prompt diagnosis. The visual assessment process of magnetic resonance imaging (MRI) is expensive in terms of time and effort and prone to delays. The integration of an AI-enabled system to help clinicians in diagnosing patients with LBP is expected to alleviate the burden on radiologists and foster a less time-consuming and cost-efficient diagnostic process. In this paper, a convolutional neural network (CNN) model has been built to diagnose T2-weighted (T2W) axial MRI scans. The model detects LSS in these scans and has achieved remarkable accuracy and recall of $\mathbf{9 1 \%}$ and $\mathbf{9 6 \%}$, respectively. Explainable AI (XAI) using LIME’s ImageExplainer was also implemented to ensure the model offers explainable insights. This is to ensure the model is not only accurate but also reliable.