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Objectives: To develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool., Methods: The three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists' diagnoses were then compared to the diagnoses of the model., Results: The model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics., Conclusions: The CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices., (© 2023. The Author(s).)

Objectives: Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiologic characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires., Methods: We evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics were compared., Results: Data from 4827 patients (4690 patients without LSS: mean age 62.44, range 27-84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30-71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91, and a Cohen's kappa of 0.85., Conclusions: Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower health care costs., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Postoperative MRI of the lumbar spine is a mainstay for detailed anatomic assessment and evaluation of complications related to decompression and fusion surgery. Key factors for reliable interpretation include clinical presentation of the patient, operative approach, and time elapsed since surgery. Yet, recent spinal surgery techniques with varying anatomic corridors to approach the intervertebral disc space and implanted materials have expanded the range of normal (expected) and abnormal (unexpected) postoperative changes. Modifications of lumbar spine MRI protocols in the presence of metallic implants, including strategies for metal artifact reduction, provide important diagnostic information. This focused review discusses essential principles for the acquisition and interpretation of MRI after lumbar spinal decompression and fusion surgery, highlights expected postoperative changes, and describes early and delayed postoperative complications with examples., (© RSNA, 2023.)

Purpose: Lumbar spinal stenosis (LSS) is a condition affecting several hundreds of thousands of adults in the United States each year and is associated with significant economic burden. The current decision-making practice to determine surgical candidacy for LSS is often subjective and clinician specific. In this study, we hypothesize that the performance of artificial intelligence (AI) methods could prove comparable in terms of prediction accuracy to that of a panel of spine experts., Methods: We propose a novel hybrid AI model which computes the probability of spinal surgical recommendations for LSS, based on patient demographic factors, clinical symptom manifestations, and MRI findings. The hybrid model combines a random forest model trained from medical vignette data reviewed by surgeons, with an expert Bayesian network model built from peer-reviewed literature and the expert opinions of a multidisciplinary team in spinal surgery, rehabilitation medicine, interventional and diagnostic radiology. Sets of 400 and 100 medical vignettes reviewed by surgeons were used for training and testing., Results: The model demonstrated high predictive accuracy, with a root mean square error (RMSE) between model predictions and ground truth of 0.0964, while the average RMSE between individual doctor's recommendations and ground truth was 0.1940. For dichotomous classification, the AUROC and Cohen's kappa were 0.9266 and 0.6298, while the corresponding average metrics based on individual doctor's recommendations were 0.8412 and 0.5659, respectively., Conclusions: Our results suggest that AI can be used to automate the evaluation of surgical candidacy for LSS with performance comparable to a multidisciplinary panel of physicians., (© 2022. The Author(s).)

Study Design: Retrospective case series., Objective: To examine the reoperation rate, specifically the need for posterior decompression and/or fusion, in a cohort of patients who underwent stand-alone lateral lumbar interbody fusion for symptomatic spinal stenosis with instability or deformity., Summary of Background Data: Lateral lumbar interbody fusion provides a minimally invasive means of achieving interbody arthrodesis and indirect foraminal decompression while avoiding the potential morbidity of traditional anterior or posterior approaches. The revision rate for formal posterior decompression after isolated lateral lumbar interbody fusion for spinal stenosis is unknown., Methods: One hundred seventeen patients who underwent stand-alone lateral lumbar interbody fusion for symptomatic spinal stenosis with an indication for fusion were included in the analysis. Detailed demographic and intraoperative data were collected. Clinical evaluation was done both preoperatively and at the final follow-up, and radiographical evaluation was done preoperatively and with the first postoperative standing radiographs., Results: A total of 10.3% of patients who underwent stand-alone lateral lumbar interbody fusion ultimately required revision surgery, most commonly for persistent radiculopathy and symptomatic implant subsidence. Average time to revision was 10.8 months. There was no difference in radiographical correction between patients who did and did not require revision surgery., Conclusion: Lateral lumbar interbody fusion provides a minimally invasive means to treat lumbar spinal stenosis with an acceptable revision rate for formal posterior decompression at early follow-up., Level of Evidence: 4.

Purpose: To report three cases of transient perioperative neurological deficit in the absence of direct cord insult following decompression of the severely stenotic thoracic spine., Methods: The clinical and radiographic electronic medical records of three patients who underwent decompression for severe midthoracic stenosis with transient neurological deficits perioperatively were reviewed. The cases are presented with consideration of possible underlying mechanisms and multimodality intraoperative monitoring (IOM) findings., Results: Two patients had neurologic changes on IOM and Stagnara wake-up test, the remaining patient had absent motor and sensory potentials at baseline and throughout the case. IOM changes were observed immediately following decompression in the absence of direct cord insult or displacement. Postoperatively all patients experienced neurological motor deficits which presented as complete paralysis of the right lower extremity in two of the patients and the left lower extremity in one patient. The deficit was transient-improvement of motor strength occurred between 1 and 13 months of follow-up in all patients., Conclusion: Decompression of a severely stenotic region of the thoracic spinal cord may lead to a complete yet transient motor deficit in the perioperative period in the absence of direct mechanical cord insult. Potential etiologies include ischemia-reperfusion injury, microthrombi, and altered perfusion due to internal recoil of spinal cord architecture following decompression. IOM may show conspicuous findings in such events, however, may not be relied upon when baseline potentials are sub-optimal. Recognition of this short-lived neurological deficit following decompression of the severely stenotic thoracic spine will improve preoperative patient counseling and merits further study for determination of the precise pathophysiology.