Development of a Machine-Learning Algorithm to Identify Cauda Equina Compression on Magnetic Resonance Imaging Scans

• Published in: World neurosurgery Vol. 195; p. 123669
• Main Authors: Biswas, Sayan, Sarkar, Ved, MacArthur, Joshua Ian, Guo, Li, Deng, Xutao, Snowdon, Ella, Ahmed, Hamza, Tetlow, Callum, George, K. Joshi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2025
• Subjects: Adult; Algorithms; Cauda equina compression; Cauda Equina Syndrome - diagnostic imaging; CNN; Female; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Male; Mid-sagittal MRI; Middle Aged; Neural Networks, Computer; Neurosurgery; Sensitivity and Specificity; CEC; CNN; MRI; Cauda equina compression; Grad-CAM; DP; CES; GIRFT; VGG; NHS; Mid-sagittal MRI; PCR; ML; Prolapse to canal ratio; Disc prolapse; Visual Geometry Group; National Health Service; Magnetic resonance imaging; Cauda equina syndrome; Machine-learning; Get It Right First Time; Gradient-weighted Class Activation Mapping
• ISSN: 1878-8750; 1878-8769; 1878-8769
• DOI: 10.1016/j.wneu.2025.123669

Cauda equina syndrome (CES) poses significant neurological risks if untreated. Diagnosis relies on clinical and radiological features. As the symptoms are often nonspecific and common, the diagnosis is usually made after a magnetic resonance imaging (MRI) scan. A huge number of MRI scans are done to exclude CES but nearly 80% of them will not have CES. This study aimed to develop and validate a machine-learning model for automated CES detection from MRI scans to enable faster triage of patients presenting with CES like clinical features. MRI scans from suspected CES patients (2017–2022) were collected and categorized into normal scans/disc protrusion (0%–50% canal stenosis) and cauda equina compression (>50% canal stenosis). A convolutional neural network was developed and tested on a total of 715 images (80:20 split). Gradient descent heatmaps were generated to highlight regions crucial for classification. The model achieved an accuracy of 0.950 (0.921–0.971), a sensitivity of 0.969 (0.941–0.987), a specificity of 0.859 (0.742–0.937), a positive predictive value of 0.969 (0.944–0.984), and an area under the curve of 0.915 (0.865–0.958). Gradient descent heatmaps demonstrated accurate identification of any clinically relevant disc herniation into the spinal canal. This study pilots a deep learning approach for predicting cauda equina compression presence, promising improved healthcare quality and timely CES management. As referrals rise, this tool can act as a fast triage system which can lead to prompt management of CES in environments where resources for radiological interpretation of MRI scans are limited.