Stanford Neurosurgical
Artificial Intelligence and Machine Learning Laboratory

The safety and efficacy of surgery is our number one priority. As we enter the era of big data, the onus is upon us to utilize what we have learned to improve medical care for future generations. Using novel, cutting edge artificial intelligence and machine learning techniques, our goal is to mine through millions of patient records to predict outcomes following all types of surgery. Imagine a virtual algorithm, that is capable of providing a true estimate of surgical risk, outcome, and efficacy – based specifically on your characteristics. This is the very foundation of precision medicine, and will empower physicians to deliver outstanding care.

Research

The focus of my laboratory is to utilize precision medicine techniques to improve the diagnosis and treatment of neurologic conditions. From traumatic brain injury to spinal scoliosis, the ability to capture detailed data regarding clinical symptoms and treatment outcomes has empowered us to do better for patients. Utilize data to do better for patients, that’s what we do.


Our Team

The Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory is led by Dr. Anand Veeravagu, an Associate Professor of Neurosurgery and Associate Professor of Orthopedic Surgery, by courtesy, and Director of Minimally Invasive NeuroSpine Surgery at Stanford. Our laboratory team includes neurosurgery residents, clinical instructors, and medical students. 

We're Hiring!

Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory is inviting applications for a 1-year post-doctoral research position. It offers an excellent opportunity for academic advancement and exposure to clinical neurosurgery. Responsibilities include clinical research productivity, database management, analytics, writing and study coordination. Highly motivated individuals with a medical degree, background analytics and prior neurosurgery experience welcomed. 

To apply, please contact us by email: neurobigdata@stanford.edu


Publications

Featured Publications

Demonstrating the successful application of synthetic learning in spine surgery for training multi-center models with increased patient privacy

From real-time tumor classification to operative outcome prediction, applications of machine learning to neurosurgery are powerful. However, the translation of many of these applications are restricted by the lack of "big data" in neurosurgery. 
 

Sigma-1 receptor expression in a subpopulation of lumbar spinal cord microglia in response to peripheral nerve injury

Sigma-1 Receptor has been shown to localize to sites of peripheral nerve injury and back pain. Radioligand probes have been developed to localize Sigma-1 Receptor and thus image pain source. However, in non-pain conditions, Sigma-1 Receptor expression has also been demonstrated in the central nervous system and dorsal root ganglion. 

 

Other Publications

Associate Professor of Neurosurgery and, by courtesy, of Orthopaedic Surgery

Publications

  • Impact of Supine versus Prone Positioning on Segmental Lumbar Lordosis in Patients Undergoing ALIF Followed by PSF: A Comparative Study. Journal of clinical medicine Sadeghzadeh, S., Yoo, K. H., Lopez, I., Johnstone, T., Schonfeld, E., Haider, G., Marianayagam, N. J., Stienen, M. N., Veeravagu, A. 2024; 13 (12)

    Abstract

    Background: Anterior lumbar interbody fusion (ALIF) and posterior spinal fusion (PSF) play pivotal roles in restoring lumbar lordosis in spinal surgery. There is an ongoing debate between combined single-position surgery and traditional prone-position PSF for optimizing segmental lumbar lordosis. Methods: This retrospective study analyzed 59 patients who underwent ALIF in the supine position followed by PSF in the prone position at a single institution. Cobb angles were measured preoperatively, post-ALIF, and post-PSF using X-ray imaging. One-way repeated measures ANOVA and post-hoc analyses with Bonferroni adjustment were employed to compare mean Cobb angles at different time points. Cohen's d effect sizes were calculated to assess the magnitude of changes. Sample size calculations were performed to ensure statistical power. Results: The mean segmental Cobb angle significantly increased from preoperative (32.2 ± 13.8 degrees) to post-ALIF (42.2 ± 14.3 degrees, Cohen's d: -0.71, p < 0.0001) and post-PSF (43.6 ± 14.6 degrees, Cohen's d: -0.80, p < 0.0001). There was no significant difference between Cobb angles after ALIF and after PSF (Cohen's d: -0.10, p = 0.14). The findings remained consistent when Cobb angles were analyzed separately for single-screw and double-screw ALIF constructs. Conclusions: Both supine ALIF and prone PSF significantly increased segmental lumbar lordosis compared to preoperative measurements. The negligible difference between post-ALIF and post-PSF lordosis suggests that supine ALIF followed by prone PSF can be an effective approach, providing flexibility in surgical positioning without compromising lordosis improvement.

    View details for DOI 10.3390/jcm13123555

    View details for PubMedID 38930084

  • Focal motor weakness and recovery following chronic subdural hematoma evacuation. Journal of neurosurgery Nisson, P. L., Francis, J., Michel, M., Patil, S., Uchikawa, H., Veeravagu, A., Bonda, D. 2024: 1-8

    Abstract

    The incidence of chronic subdural hematomas (cSDHs) is expected to climb precipitously in the coming decades because of the aging populous. Neurological weakness is one of the most common presenting neurological symptoms of cSDH. Yet, the recovery rates of motor strength recovery are seldom documented, as neurological outcomes have predominantly focused on broader functional assessment scores or mortality. In this study, the authors performed one of the first detailed analyses on functional motor weakness and recovery in patients who underwent cSDH evacuation.Patients who underwent evacuation of a cSDH at a tertiary academic medical center between November 2013 and December 2021 were retrospectively identified using ICD-9 and ICD-10 billing codes. The presence of focal motor weakness was subcategorized by location as upper extremity (UE) or lower extremity (LE). Postoperative improvement, worsening, or resolution of weakness was recorded at the time of discharge. Statistical analysis included univariate and backward stepwise multivariable logistic regression modeling.A total of 311 patients were included in the analysis. Patients were significantly more likely to experience UE weakness than LE weakness (29% vs 18%, p < 0.001). Forty-one percent (43/104) had both UE and LE weakness present. Risk factors for the development of focal motor weakness at the time of presentation were older age (OR 1.02, p = 0.03), increased cSDH size (OR 1.04, p = 0.02), and the presence of a unilateral cSDH (OR 2.32, p = 0.008). The majority of patients (68%, 71/104) experienced motor strength improvement following cSDH evacuation, with 58% (60/104) having complete resolution of weakness. Multivariable logistic regression analysis revealed that longer symptom duration was associated with lower rates of improvement (OR 0.96, p = 0.024). Older age was also associated with reduced resolution of weakness (OR 0.96, p = 0.02).This study represents one of the first in-depth analyses investigating the rates of motor strength weakness and recovery following cSDH evacuation. Nearly two-thirds of all patients had complete resolution of their weakness by the time of discharge, and more than three-quarters had partial improvement. Risk factors for impaired neurological recovery were longer symptom duration prior to treatment and older age.

    View details for DOI 10.3171/2024.4.JNS24121

    View details for PubMedID 38875718

  • CyberKnife stereotactic radiosurgery for extramedullary plasmacytoma in the external auditory canal: illustrative case. Journal of neurosurgery. Case lessons Patil, S., Shaghaghian, E., Yuan, L., Shah, A., Marianayagam, N. J., Park, D. J., Soltys, S. G., Veeravagu, A., Gibbs, I. C., Li, G., Chang, S. D. 2024; 7 (19)

    Abstract

    Plasmacytoma, a rare plasma cell disorder, often presents as a solitary or multiple tumors within the bone marrow or soft tissues, typically associated with multiple myeloma. Extramedullary plasmacytomas (EMPs), particularly those located in the external auditory canal (EAC), are exceedingly rare and pose significant treatment challenges given their location, anatomical complexity, and high risk of recurrence.The authors report the case of a 72-year-old male with a history of multiple myeloma, presenting with recurrent left EAC plasmacytoma. After initial conventional radiotherapy for the lesion, a recurrence was documented in 7 years. The patient subsequently underwent stereotactic radiosurgery, which proved successful, leading to complete resolution of the lesion without any long-term adverse effects or radiation-related complications over a 45-month period.This case is a unique instance of utilizing stereotactic radiosurgery for recurrent EMP in the EAC, highlighting its potential as an effective approach in managing complex plasmacytomas.

    View details for DOI 10.3171/CASE2479

    View details for PubMedID 38710109

    View details for PubMedCentralID PMC11076403

  • Experience with the utilization of new-generation shared-control robotic system for spinal instrumentation. Journal of neurosurgical sciences Haider, G., Shah, V., Lopez, I., Wagner, K. E., Stienen, M. N., Veeravagu, A. 2024

    Abstract

    Robotic assistance in spine surgery is emerging as an accurate, effective and enabling technology utilized in the treatment of patients with surgical spinal pathology. The safety and reproducibility of robotic assistance in the placement of pedicle screw instrumentation is still being investigated. The objective of this study was to present our experience of instrumented spinal fusion utilizing an intraoperative robotic guidance system.We retrospectively reviewed all cases of spinal instrumentation of the thoracic and lumbo-sacral spine using the Mazor X robotic system (Medtronic Inc, Minneapolis, MN, USA), performed at our institution by one surgeon between July 2017 and June 2020. Wilcoxon Rank test was used to compare time taken to place each screw during the first 20 cases and the cases thereafter.A total of 28 patients were included. A total of 159 screws were placed using the Mazor X robotic system. The overall mean time for screw placement was 7.8±2.3 minutes and there was a significant reduction in the mean time for screw placement after the 20th case or 120 screws (8.70 vs. 5.42 min, P=0.008). No postoperative neurologic deficit or new radiculopathy was noted to occur secondary to hardware placement. No revision surgery was required for replacement or removal of a mispositioned screw.From this single-center, single-surgeon series we conclude that robot-assisted spine surgery can be safely and efficiently integrated into the operating room workflow, which improves after a learning curve of approximately 20 operative interventions. We found robot-assisted spinal instrumentation to be reliable, safe, effective and highly precise.

    View details for DOI 10.23736/S0390-5616.24.06206-4

    View details for PubMedID 38619188

  • Deep Learning Prediction of Cervical Spine Surgery Revision Outcomes Using Standard Laboratory and Operative Variables. World neurosurgery Schonfeld, E., Shah, A., Johnstone, T. M., Rodrigues, A., Morris, G. K., Stienen, M. N., Veeravagu, A. 2024

    Abstract

    INTRODUCTION: Cervical spine procedures represent a major proportion of all spine surgery. Mitigating the revision rate following cervical procedures requires careful patient selection. While complication risk has successfully been predicted, revision risk has proven more challenging. This is likely due to the absence of granular variables in claims databases. The objective of this study was to develop a state-of-the-art of revision prediction of cervical spine surgery using laboratory and operative variables.METHODS: Using the Stanford Research Repository, patients undergoing a cervical spine procedure between 2016-2022 were identified (N=3151) and recent laboratory values were collected. Patients were classified into separate cohorts by revision outcome and timeframe. Machine and deep learning models were trained to predict each revision outcome from laboratory and operative variables.RESULTS: Red blood cell count, Hemoglobin, Hematocrit, Mean Corpuscular Hemoglobin Concentration, Red Blood Cell Distribution Width, Platelet Count, CO2, Anion Gap, and Calcium were all significantly associated with one or more revision cohorts. For the prediction of 3-month revision, the deep neural network achieved AUC of 0.833. The model demonstrated increased performance for anterior than posterior and arthrodesis than decompression procedures.CONCLUSIONS: Our deep learning approach successfully predicted 3-month revision outcomes from demographic variables, standard laboratory values, and operative variables, in a cervical spine surgery cohort. This work introduces standard laboratory values and operative codes as meaningful predictive variables for revision outcome prediction. The increased performance on certain procedures evidences the need for careful development and validation of "one-size-fits-all" risk scores for spine procedures.

    View details for DOI 10.1016/j.wneu.2024.02.112

    View details for PubMedID 38408699