Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

STUDY DESIGN: Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine. OBJECTIVE: To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. MR imaging is the most common imaging technique used in investigating low back pain (LBP). Various features of degradation, based on MRIs, are commonly recorded and graded, e.g., Modic change and Pfirrmann grading of intervertebral discs. Consistent scoring and grading is important for developing robust clinical systems and research. Automation facilitates this consistency and reduces the time of radiological analysis considerably and hence the expense. METHODS: 12,018 intervertebral discs, from 2009 patients, were graded by a radiologist and were then used to train: (1) a system to detect and label vertebrae and discs in a given scan, and (2) a convolutional neural network (CNN) model that predicts several radiological gradings. The performance of the model, in terms of class average accuracy, was compared with the intra-observer class average accuracy of the radiologist. RESULTS: The detection system achieved 95.6% accuracy in terms of disc detection and labeling. The model is able to produce predictions of multiple pathological gradings that consistently matched those of the radiologist. The model identifies 'Evidence Hotspots' that are the voxels that most contribute to the degradation scores. CONCLUSIONS: Automation of radiological grading is now on par with human performance. The system can be beneficial in aiding clinical diagnoses in terms of objectivity of gradings and the speed of analysis. It can also draw the attention of a radiologist to regions of degradation. This objectivity and speed is an important stepping stone in the investigation of the relationship between MRIs and clinical diagnoses of back pain in large cohorts. LEVEL OF EVIDENCE: Level 3.

Original publication




Journal article


Eur Spine J

Publication Date





1374 - 1383


Automated grading, Deep learning, Disc analysis, Disc bulge, Disc classification, Disc detection, Disc herniation, Modic changes, Pfirrmann grading, Spondylolisthesis, Vertebrae analysis, Bone Marrow, Humans, Intervertebral Disc, Intervertebral Disc Degeneration, Lumbar Vertebrae, Magnetic Resonance Imaging, Male, Middle Aged, Neural Networks (Computer), Radiologists, Spinal Stenosis, Spondylolisthesis