Artificial Intelligence Comparison of the Radiologist Report With Endoscopic Predictors of Successful Transforaminal Decompression for Painful Conditions of the Lumber Spine: Application of Deep Learning Algorithm Interpretation of Routine Lumbar Magnetic Resonance Imaging Scan

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Abstract
Background 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 radiologist9s report with a review of endoscopic clinical outcomes. Objective To analyze and compare the probability of predicting successful outcome with lumbar spinal endoscopy by using the radiologist9s MRI grading and interpretation of the radiologic image with a novel AI deep learning neural network (Multus Radbot™) as independent prognosticators. Methods The location and severity of foraminal stenosis were analyzed using comparative ordinal grading by the radiologist, and a contiguous grading by the AI network in patients suffering from lateral recess and foraminal stenosis due to lumbar herniated disc. The compressive pathology definitions were extracted from the radiologist lumbar MRI reports from 65 patients with a total of 383 levels for the central canal – (0) no disc bulge/protrusion/canal stenosis, (1) disc bulge without canal stenosis, (2) disc bulge resulting in canal stenosis, and (3) disc herniation/protrusion/extrusion resulting in canal stenosis. Both neural foramina were assessed with either – (0) neural foraminal stenosis absent, or (1) neural foramina are stenosis present. Reporting criteria for the pathologies at each disc level and, when available, the grading of severity were extracted and assigned into two categories: “Normal,” and “Stenosis.” Clinical outcomes were graded using dichotomized modified Macnab criteria considering Excellent and Good results as “Improved,” and Fair and Poor outcomes as “Not Improved.” Binary logistic regression analysis was used to predict the probability of the AI- and radiologist grading of stenosis at the 88 foraminal decompression sites to result in “Improved” outcomes. Results The average age of the 65 patients was 62.7 +/- 12.7 years. They consisted of 51 (54.3%) males and 43 (45.7%) females. 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. Conclusions Deep learning algorithms are 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. Level of Evidence 3. Clinical Relevance Validity, clinical teaching, evaluation study.