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.


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.

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


  • Getting What You Pay For: Impact of Copayments on Physical Therapy and Opioid Initiation, Timing, and Continuation for Newly Diagnosed Low Back Pain. The spine journal : official journal of the North American Spine Society Jin, M. C., Jensen, M., Barros Guinle, M. I., Ren, A., Zhou, Z., Zygourakis, C. C., Desai, A. M., Veeravagu, A., Ratliff, J. K. 2024


    Physical therapy (PT) is an important component of low back pain (LBP) management. Despite established guidelines, heterogeneity in medical management remains common.We sought to understand how copayments impact timing and utilization of PT in newly diagnosed LBP.The IBM Watson Health MarketScan claims database was utilized in a longitudinal setting.Adult patients with LBP.The primary outcomes-of-interest were timing and overall utilization of PT services. Additional outcomes-of-interest included timing of opioid prescribing.Actual and inferred copayments based on non-PCP visit claims were used to evaluate the relationship between PT copayment and incidence of PT initiation. Multivariable regression models were used to evaluate factors influencing PT usage.Overall, 2,467,389 patients were included. PT initiation, among those with at ≥1 PT service during the year after LBP diagnosis (30.6%), occurred at a median of 8 days post-diagnosis (IQR 1-55). Among those with at least one PT encounter, incidence of subsequent PT visits was significantly lower for those with high initial PT copayments. High initial PT copayments, while inversely correlated with PT utilization, were directly correlated with subsequent opioid use (0.77 prescriptions/patient [

  • An Integrated 3-Dimentional Navigation System Increases the Accuracy, Efficiency, and Safety of Percutaneous Thoracolumbar Pedicle Screw Placement in Minimally Invasive Approaches: A Randomized Cadaveric Study. Global spine journal Lakomkin, N., Eastlack, R. K., Uribe, J. S., Park, P., Ryu, S. I., Kretzer, R., Mimran, R. I., Holman, P., Veeravagu, A., Hassanzadeh, H., Johnson, M. M., Sullivan, L., Clark, A., Mundis, G. M. 2024: 21925682231224394


    STUDY DESIGN: Cadaveric study.OBJECTIVES: The purpose of this study was to compare a novel, integrated 3D navigational system (NAV) and conventional fluoroscopy in the accuracy, efficiency, and radiation exposure of thoracolumbar percutaneous pedicle screw (PPS) placement.METHODS: Twelve skeletally mature cadaveric specimens were obtained for twelve individual surgeons. Each participant placed bilateral PS at 11 segments, from T8 to S1. Prior to insertion, surgeons were randomized to the sequence of techniques and the side (left or right). Following placement, a CT scan of the spine was obtained for each cadaver, and an independent reviewer assessed the accuracy of screw placement using the Gertzbein grading system. Outcome metrics of interest included a comparison of breach incidence/severity, screw placement time, total procedure time, and radiation exposure between the techniques. Bivariate statistics were employed to compare outcomes at each level.RESULTS: A total of 262 screws (131 using each technique) were placed. The incidence of cortical breaches was significantly lower with NAV compared to FG (9% vs 18%; P = .048). Of breaches with NAV, 25% were graded as moderate or severe compared to 39% in the FG subgroup (P = .034). Median time for screw placement was significantly lower with NAV (2.7 vs 4.1 min/screw; P = .012), exclusive of registration time. Cumulative radiation exposure to the surgeon was significantly lower for NAV-guided placement (9.4 vs 134 muGy, P = .02).CONCLUSIONS: The use of NAV significantly decreased the incidence of cortical breaches, the severity of screw breeches, screw placement time, and radiation exposure to the surgeon when compared to traditional FG.

    View details for DOI 10.1177/21925682231224394

    View details for PubMedID 38165219

  • Stanford University School of Medicine: Our Neurosurgical Heritage. Neurosurgery Veeravagu, A., Kim, L. H., Rao, V. L., Lim, M., Shuer, L. M., Harris, O. A., Steinberg, G. K. 2023


    The legacy of Stanford University's Department of Neurosurgery began in 1858, with the establishment of a new medical school on the West Coast. Stanford Neurosurgery instilled an atmosphere of dedication to neurosurgical care, scientific research, education, and innovation. We highlight key historical events leading to the formation of the medical school and neurosurgical department, the individuals who shaped the department's vision and expansion, as well as pioneering advances in research and clinical care. The residency program was started in 1961, establishing the basis of the current education model with a strong emphasis on training future leaders, and the Moyamoya Center, founded in 1991, became the largest Moyamoya referral center in the United States. The opening of Stanford Stroke Center (1992) and seminal clinical trials resulted in a significant impact on cerebrovascular disease by expanding the treatment window of IV thrombolysis and intra-arterial thrombectomy. The invention and implementation of CyberKnife® (1994) marks another important event that revolutionized the field of radiosurgery, and the development of Stanford's innovative Brain Computer Interface program is pushing the boundaries of this specialty. The more recent launch of the Neurosurgery Virtual Reality and Simulation Center (2017) exemplifies how Stanford is continuing to evolve in this ever-changing field. The department also became a model for diversity within the school as well as nationwide. The growth of Stanford Neurosurgery from one of the youngest neurosurgery departments in the country to a prominent comprehensive neurosurgery center mirrors the history of neurosurgery itself: young, innovative, and willing to overcome challenges.

    View details for DOI 10.1227/neu.0000000000002799

    View details for PubMedID 38095422

  • Type II Odontoid Fractures in the Elderly Presenting to the Emergency Department: An Assessment of Factors Affecting In-Hospital Mortality and Discharge to Skilled Nursing Facilities. The spine journal : official journal of the North American Spine Society Johnstone, T., Shah, V., Schonfeld, E., Sadeghzadeh, S., Haider, G., Stienen, M., Marianayagam, N. J., Veeravagu, A. 2023


    Type II odontoid fractures (OF) are among the most common cervical spine injuries in the geriatric population. However, there is a paucity of literature regarding their epidemiology. Additionally, the optimal management of these injuries remains controversial, and no study has evaluated the short-term outcomes of geriatric patients presenting to emergency departments (ED).This study aims to document the epidemiology of geriatric patients presenting to EDs with type II OFs and determine whether surgical management was associated with early adverse outcomes such as in-hospital mortality and discharge to skilled nursing facilities (SNF).This is a retrospective cohort study.Data was used from the 2016-2020 Nationwide Emergency Department Sample. Patient encounters corresponding to type II OFs were identified. Patients younger than 65 at the time of presentation to the ED and those with concomitant spinal pathology were excluded.The association between the surgical management of geriatric type II OFs and outcomes such as in-hospital mortality and discharge to SNFs.Patient, fracture, and surgical management characteristics were recorded. A propensity score matched cohort was constructed to reduce differences in age, comorbidities, and injury severity between patients undergoing operative and nonoperative management. Additionally, to develop a positive control for the analysis of geriatric patients with type II OFs and no other concomitant spinal pathology, a cohort of patients that had been excluded due to the presence of a concomitant spinal cord injury (SCI) was also constructed. Multivariate regressions were then performed on both the matched and unmatched cohorts to ascertain the associations between surgical treatment and in-hospital mortality, inpatient length of stay, encounter charges, and discharge to SNFs.11,325 encounters were included. The mean total charge per encounter was

  • Accuracy of predicted postoperative segmental lumbar lordosis in spinal fusion using an intraoperative robotic planning and guidance system. Journal of neurosurgical sciences Haider, G., Shah, V., Johnstone, T., Maldaner, N., Stienen, M., Veeravagu, A. 2023


    Restoring lumbar lordosis is one of the main goals in lumbar spinal fusion surgery. The Mazor X-AlignTM software allows for the prediction of postoperative segmental lumbar lordosis based on preoperative imaging. There is limited data on the accuracy of this preoperative prediction, especially in patients undergoing short segment lumbar fusion. The objective of our study was to determine the accuracy of predicted postoperative segmental lumbar lordosis using the Mazor X-AlignTM software in patients requiring short segmental fusion.Retrospective analysis of adult patients undergoing pedicle screw spinal instrumentation of not more than four levels using the Mazor XTM Robot (Medtronic Inc., Minneapolis, MN, USA) between July 2017 to June 2020. The robotic guidance software, Mazor X-AlignTM (Medtronic Inc., Minneapolis, MN, USA) was used to calculate the predicted segmental lumbar lordosis based on preoperative CT-imaging and the plan was executed under intraoperative robotic guidance. Predicted segmental lumbar lordosis was compared to achieved segmental lumbar lordosis on 1-month postoperative x-rays using the Cobb angle methodology.A total of 15 patients (46.6% female) with a mean age of 61.5±10.9 years were included. All patients underwent posterior lumbo-sacral spinal fusion with the Mazor XTM robotic system with 11 patients (73.3%) undergoing anterior column reconstruction prior to posterior fixation. Instrumentation was performed across a mean of 2.6 levels per case. Preoperative, the mean segmental lumbar lordosis was 30.2±13.6 degrees. The mean planned segmental lumbar lordosis was 35.5±17.0 degrees while the mean achieved segmental lumbar lordosis was 35.8±16.7 degrees. There was no significant mean difference between the planned and achieved segmental lumbar lordosis (P=0.334).The Mazor XTM intraoperative robotic planning and guidance is accurate in predicting postoperative segmental lumbar lordosis after short segmental fusion. Our findings may assure surgical decision making and planning.

    View details for DOI 10.23736/S0390-5616.23.06142-8

    View details for PubMedID 37997323

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: