Take a look at the clinical study that was just published in the International Journal of Spine Surgery!

Identifying pain generators in multilevel lumbar degenerative disc disease is not trivial but is crucial for lasting symptom relief with the targeted endoscopic spinal decompression surgery. Artificial intelligence (AI) applications of deep learning neural networks to the analysis of routine lumbar MRI scans could help the primary care and endoscopic specialist physician to compare the radiologist's report with a review of endoscopic clinical outcomes. The objective of this study was to analyze and compare the probability of predicting successful outcome with lumbar spinal endoscopy by using the radiologist's MRI grading and interpretation of the radiologic image with a novel AI deep learning neural network (Multus Radbot™) as independent prognosticators.

At an average final follow-up of 57.4 +/- 12.57, Macnab outcome analysis showed that 86.4% of the 88 foraminal decompressions resulted in Excellent and Good (Improved) clinical outcomes. The stenosis grading by the radiologist showed an average severity score of 4.71 +/- 2.626, and the average AI severity grading was 5.65 +/- 3.73. Logit regression probability analysis of the two independent prognosticators showed that both the grading by the radiologist (86.2%; odds ratio 1.264) and the AI grading (86.4%; odds ratio 1.267) were nearly equally predictive of a successful outcome with the endoscopic decompression.

The deep learning algorithms developed by Aptus and Multus were shown to be capable of identifying lumbar foraminal compression due to herniated disc. The treatment outcome was correlated to the decompression of the directly visualized corresponding pathology during the lumbar endoscopy. This research should be extended to other validated pain generators in the lumbar spine.

Read the full paper here on the International Journal of Spine Surgery website