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.
Trends in Anterior Lumbar Interbody Fusion in the United States: A MarketScan Study From 2007 to 2014.
Clinical spine surgery
BACKGROUND: Although the incidence of spinal fusions has increased significantly in the United States over the last quarter century, national trends of anterior lumbar interbody fusion (ALIF) utilization are not known.PURPOSE: The objective of this study was to characterize trends, clinical characteristics, risk factors associated with, and outcomes of ALIF in the United States.STUDY DESIGN: This was an epidemiological study using national administrative data from the MarketScan database.METHODS: Using a large administrative database, we identified adults who underwent ALIF in the United States from 2007 to 2014. The incidence of ALIF was studied longitudinally over time and across geographic regions in the United States. Data related to postoperative complications, length of stay, readmission, and cost were collected.RESULTS: We identified 49,945 patients that underwent ALIF in the United States between 2007 and 2014. The total number of ALIF procedures increased from 3650 in 2007 to 6151 in 2014, accounting for an average increase of 24.07% annually. The Southern United States performed the highest number of ALIFs. The most common conditions treated were degenerative disc disease and spondylolisthesis. Over one third of patients (34.6%) underwent multilevel fusion. The most common complications were iron deficiency anemia, urinary tract infections, and pulmonary complications. Hospital and physician pay increased significantly during the study period.CONCLUSIONS: For the first time in our knowledge, we identified national trends in ALIF utilization, outcomes, and cost using a large administrative database. Our study reaffirms prior work that has demonstrated low rates of complications, mortality, and readmission following ALIF.LEVEL OF EVIDENCE: Level III.
View details for DOI 10.1097/BSD.0000000000000904
View details for PubMedID 31609798
A Descriptive Analysis of Spinal Cord Arteriovenous Malformations: Clinical Features, Outcomes, and Trends in Management.
BACKGROUND: Spinal arteriovenous malformations (AVM) are an abnormal interconnection of vasculature in the spine than can lead to significant neurological deficit if left untreated.OBJECTIVE: The objective of this study was to characterize how spinal AVM patients initially presented, what treatment options were utilized, and their overall outcomes on a national scale.METHODS: The MarketScan database was queried to identify adult patients diagnosed with a spinal AVM from 2007 - 2015. Trends in management, postoperative complication rates, and costs were determined.RESULTS: 976 patients were identified with having a diagnosis of a spinal AVM. Patients were more commonly treated with an open incision than an embolization (40.1% vs 15.4%). The overall complication rate was 33.61%. Spinal AVM admissions have been stable over the past decade and mean cost of hospitalization has risen from of
- Laminectomy versus Corpectomy for Spinal Metastatic Disease-Complications, Costs, and Quality Outcomes. World neurosurgery 2019 Hide More
Grade II Spondylolisthesis: Reverse Bohlman Procedure with Trans-Discal S1-L5 and S2Ai Screws Placed with Robotic Guidance.
STUDY DESIGN: Technical Report with two illustrative cases.OBJECTIVE: Grade II spondylolisthesis remains a complex surgical pathology for which there is no consensus regarding optimal surgical strategies. Surgical strategies vary regarding extent of reduction, utilization of instrumentation/interbody support, and anterior versus posterior approaches with or without decompression. The objective of this study is to provide the first report on the efficacy of robotic spinal surgery systems in supporting the treatment of grade II spondylolisthesis.METHODS: Utilizing two illustrative cases, we provide a technical report of how a robotic spinal surgery platform can be utilized to treatment grade II spondylolisthesis with a novel instrumentation strategy.RESULTS: We describe how utilization of the "Reverse Bohlman" technique to achieve a large anterior fusion construct spanning the pathologic level and buttressed by the adjacent level above, coupled with a novel, high fidelity posterior fixation scheme with transdiscal S1-L5 and S2Ai screws placed in a minimally invasive fashion with robot guidance allows for the best chance of fusion in situ.CONCLUSIONS: The "Reverse Bohlman" technique coupled with transdiscal S1-L5 and S2Ai screw fixation accomplishes the surgical goals of creating a solid fusion construct, avoiding neurologic injury with aggressive reduction, and halting the progression of anterolisthesis. Utilization of robot guidance allows for efficient placement of these difficult screw trajectories in a minimally invasive fashion.
View details for DOI 10.1016/j.wneu.2019.07.229
View details for PubMedID 31398524
- Objective measures of functional impairment for degenerative diseases of the lumbar spine: a systematic review of the literature SPINE JOURNAL 2019; 19 (7): 1276–93 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.