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.

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

Publications

  • Factors Which Predict Adverse Outcomes in Anterior Cervical Discectomy and Fusion Procedures in the Nonelderly Adult Population. Clinical spine surgery Rodrigues, A. J., Jokhai, R., Varshneya, K., Stienen, M. N., Veeravagu, A. 2022

    Abstract

    STUDY DESIGN: Retrospective cohort.OBJECTIVE: The largest published cohort of anterior cervical discectomy and fusion (ACDF) patients was queried to better characterize demographic and operative factors that predict 90-day complication and 2-year reoperation risk.SUMMARY OF BACKGROUND DATA: The MarketScan Database was queried from 2007 to 2016 to identify adult patients until 65 years, who underwent an ACDF procedure using International Classification of Diseases 9th Version (ICD-9) and Current Procedural Terminology (CPT) codes. MarketScan is a national insurance claims database that contains millions of patient records across all 50 states.METHODS: Multivariate logistic regression was used to identify factors associated with complications until 90 days and reoperations until 2 years.RESULTS: Of 138,839 ACDF procedures, 8500 patients (6.1%) experienced a complication within 90 days of the ACDF, and 7433 (5.4%) underwent surgical revision by 2 years. While the use of anterior cervical plating did not predict 2-year reoperation, it was associated with dramatically reduced 90-day complication risk (adjusted odds ratio [aOR]: 0.32; 95% confidence interval [CI]: 0.30-0.34;P<0.001). Upon multivariate analysis, female sex (aOR: 0.83; 95% CI: 0.79-0.87;P<0.001) was associated with decreased risk of 2-year reoperation, while depression predicted a 50% increase in reoperation risk (aOR: 1.51; 95% CI: 1.43-1.59;P<0.001). The single largest factor associated with reoperation risk, however, was the presence of a 90-day postoperative complication (aOR: 1.79; 95% CI: 1.66-1.94;P<0.001).CONCLUSION: Increased patient comorbidities and the use of bone morphogenic protein were found to increase the risk for postoperative complications, while cervical plating was associated with a strong decline in this risk. In addition, poor patient mental health outweighed the adverse of impact of other comorbidities on 2-year revision risk. The presence of a postoperative complication was the key modifiable risk factor associated with reoperation risk. Conclusions from this study may help surgeons better identify high-risk ACDF patients for more careful patient selection, counseling, informed consent, and management.

    View details for DOI 10.1097/BSD.0000000000001326

    View details for PubMedID 35385403

  • Commentary: Robotic Nerve Sheath Tumor Resection With Intraoperative Neuromonitoring: Case Series and Systematic Review. Operative neurosurgery (Hagerstown, Md.) Wagner, K. E., Haider, G., Veeravagu, A. 2022

    View details for DOI 10.1227/ons.0000000000000164

    View details for PubMedID 35316253

  • Vertebrae segmentation in reduced radiation CT imaging for augmented reality applications. International journal of computer assisted radiology and surgery Schonfeld, E., de Lotbiniere-Bassett, M., Jansen, T., Anthony, D., Veeravagu, A. 1800

    Abstract

    PURPOSE: There is growing evidence for the use of augmented reality (AR) navigation in spinal surgery to increase surgical accuracy and improve clinical outcomes. Recent research has employed AR techniques to create accurate auto-segmentations, the basis of patient registration, using reduced radiation dose intraoperative computed tomography images. In this study, we aimed to determine if spinal surgery AR applications can employ reduced radiation dose preoperative computed tomography (pCT) images.METHODS: We methodically decreased the imaging dose, with the addition of Gaussian noise, that was introduced into pCT images to determine the image quality threshold that was required for auto-segmentation. The Gaussian distribution's standard deviation determined noise level, such that a scalar multiplier (L: [0.00, 0.45], with steps of 0.03) simulated lower doses as L increased. We then enhanced the images with denoising algorithms to evaluate the effect on the segmentation.RESULTS: The pCT radiation dose was decreased to below the current lowest clinical threshold and the resulting images produced segmentations that were appropriate for input into AR applications. This held true at simulated dose L=0.06 (estimated 144 mAs) but not at L=0.09 (estimated 136 mAs). The application of denoising algorithms to the images resulted in increased artifacts and decreased bone density.CONCLUSIONS: The pCT image quality that is required for AR auto-segmentation is lower than that which is currently employed in spinal surgery. We recommend a reduced radiation dose protocol of approximately 140 mAs. This has the potential to reduce the radiation experienced by patients in comparison to procedures without AR support. Future research is required to identify the specific, clinically relevant radiation dose thresholds required for surgical navigation.

    View details for DOI 10.1007/s11548-022-02561-y

    View details for PubMedID 35025073

  • A Discussion of Machine Learning Approaches for Clinical Prediction Modeling. Acta neurochirurgica. Supplement Jin, M. C., Rodrigues, A. J., Jensen, M., Veeravagu, A. 2022; 134: 65-73

    Abstract

    While machine learning has occupied a niche in clinical medicine for decades, continued method development and increased accessibility of medical data have led to broad diversification of approaches. These range from humble regression-based models to more complex artificial neural networks; yet, despite heterogeneity in foundational principles and architecture, the spectrum of machine learning approaches to clinical prediction modeling have invariably led to the development of algorithms advancing our ability to provide optimal care for our patients. In this chapter, we briefly review early machine learning approaches in medicine before delving into common approaches being applied for clinical prediction modeling today. For each, we offer a brief introduction into theory and application with accompanying examples from the medical literature. In doing so, we present a summarized image of the current state of machine learning and some of its many forms in medical predictive modeling.

    View details for DOI 10.1007/978-3-030-85292-4_9

    View details for PubMedID 34862529

  • Commentary: Loss of Relativity: The Physician Fee Schedule, the Neurosurgeon, and the Trojan Horse NEUROSURGERY Tumialan, L. M., Veeravagu, A., Ratliff, J. K. 2021; 89 (6): E323-E324

Our Team

The Stanford Neurosurgical Ariticial Intelligence and Machine Learning Laboratory is led by Dr. Anand Veeravagu, an Assistant Professor of Neurosurgery and Assistant 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!

We are currently looking for post-doctoral fellows looking to build their career in health policy research with specific attention to neurologic diseases. To apply, please contact us by email: neurobigdata@stanford.edu