Detection of Degenerative Changes on MR Images of the Lumbar Spine with a Convolutional Neural Network: A Feasibility Study

There is a growing interest in machine-learning algorithms supporting radiologists in their daily work. In the current study, we found that support of diagnosis of various lumbar degenerative changes is feasible with moderate to high diagnostic accuracy using a single comprehensive CNN. Additionally, the high NPVs make this CNN a promising approach to rule out vertebral pathologies in clinical practice.

Clinical Assessment of CoLumbo Deep Learning System for Central Canal Stenosis Diagnostics

This prospective study showed that the assessment of the radiologists supported by deep learning system for central stenosis classification results in high kappa agreement. The introduction into practice of such AI-based tools would precisely predict the presence of stenosis and thus decrease the observer variability in assessing lumbar spinal stenosis severity based on MRI and its relation to cross-sectional spinal canal area. This would result in timely and effective surgical treatment and improved quality of life for these patients.