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.
Machine learning in neurosurgery: a global survey.
BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use.METHODS: The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS).RESULTS: Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging.CONCLUSIONS: This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.
View details for DOI 10.1007/s00701-020-04532-1
View details for PubMedID 32812067
Predictors of 2-year reoperation in Medicare patients undergoing primary thoracolumbar deformity surgery.
Journal of neurosurgery. Spine
OBJECTIVE: This was a retrospective cohort study in which the authors used a nationally representative administrative database. Their goal was to identify the risk factors for reoperation in Medicare patients undergoing primary thoracolumbar adult spinal deformity (ASD) surgery. Previous literature reports estimate that 20% of patients undergoing thoracolumbar ASD correction undergo revision surgery within 2 years. Most published data discuss risk factors for revision surgery in the general population, but these have not been explored specifically in the Medicare population.METHODS: Using the MarketScan Medicare Supplemental database, the authors identified patients who were diagnosed with a spinal deformity and underwent ASD surgery between 2007 and 2015. The interactions of patient demographics, surgical factors, and medical factors with revision surgery were investigated during the 2 years following primary ASD surgery. The authors excluded patients without Medicare insurance and those with any prior history of trauma or tumor.RESULTS: Included in the data set were 2564 patients enrolled in Medicare who underwent ASD surgery between 2007 and 2015. The mean age at diagnosis with spinal deformity was 71.5 years. A majority of patients (68.5%) were female. Within 2 years of follow-up, 661 (25.8%) patients underwent reoperation. Preoperative osteoporosis (OR 1.58, p < 0.0001), congestive heart failure (OR 1.35, p = 0.0161), and paraplegia (OR 2.41, p < 0.0001) independently increased odds of revision surgery. The use of intraoperative bone morphogenetic protein was protective against reoperation (OR 0.71, p = 0.0371). Among 90-day postoperative complications, a wound complication was the strongest predictor of undergoing repeat surgery (OR 2.85, p = 0.0061). The development of a pulmonary embolism also increased the odds of repeat surgery (OR 1.84, p = 0.0435).CONCLUSIONS: Approximately one-quarter of Medicare patients with ASD who underwent surgery required an additional spinal surgery within 2 years. Baseline comorbidities such as osteoporosis, congestive heart failure, and paraplegia, as well as short-term complications such as pulmonary embolism and wound complications significantly increased the odds of repeat surgery.
View details for DOI 10.3171/2020.5.SPINE191425
View details for PubMedID 32707541
- A Comparative Analysis of Patients Undergoing Fusion for Adult Cervical Deformity by Approach Type GLOBAL SPINE JOURNAL 2020 Hide More
Objective activity tracking in spine surgery: a prospective feasibility study with a low-cost consumer grade wearable accelerometer.
2020; 10 (1): 4939
Patient-reported outcome measures (PROMs) are commonly used to estimate disability of patients with spinal degenerative disease. Emerging technological advances present an opportunity to provide objective measurements of activity. In a prospective, observational study we utilized a low-cost consumer grade wearable accelerometer (LCA) to determine patient activity (steps per day) preoperatively (baseline) and up to one year (Y1) after cervical and lumbar spine surgery. We studied 30 patients (46.7% male; mean age 57 years; 70% Caucasian) with a baseline activity level of 5624 steps per day. The activity level decreased by 71% in the 1st postoperative week (p<0.001) and remained 37% lower in the 2nd (p<0.001) and 23% lower in the 4th week (p=0.015). At no time point until Y1 did patients increase their activity level, compared to baseline. Activity was greater in patients with cervical, as compared to patients with lumbar spine disease. Age, sex, ethnic group, anesthesia risk score and fusion were variables associated with activity. There was no correlation between activity and PROMs, but a strong correlation with depression. Determining activity using LCAs provides real-time and longitudinal information about patient mobility and return of function. Recovery took place over the first eight postoperative weeks, with subtle improvement afterwards.
View details for DOI 10.1038/s41598-020-61893-4
View details for PubMedID 32188895
- Postoperative Complication Burden, Revision Risk, and Health Care Use in Obese Patients Undergoing Primary Adult Thoracolumbar Deformity Surgery GLOBAL SPINE JOURNAL 2020 Hide More
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.