Data Science Programs

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Data Science Programs
Data Science Scholar Program

The use of advanced data science techniques for research and practice improvement in neurosurgery continues to expand. Obtaining training in basic and advanced data science requires both mentorship and financial support, and the CNS Scholarship in Data Science provides both on an individual basis as we seek to create a cadre of neurosurgeons with expertise in this subject.

Goals & Objectives

Give a small group of future neurosurgeons’ expertise in the application of advanced data science techniques to the field of neurosurgery

Provide those future leaders a platform within the CNS to build additional educational content and technology broadly applicable to the entire CNS membership

Improve the quality of data science research presented at the CNS Annual Meeting and published in Neurosurgery, aiming to remain at the forefront of this field

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Data Science Scholar Program Awardees

Lauren Stone, MD

University of California, San Diego

TBD

Andrew Abumoussa, MD MSc

University of North Carolina at Chapel Hill

Dr. Abumoussa is currently studying how to apply machine learning and computer vision techniques to create a global motion-artifact reduction algorithm for Digital Subtraction Angiography. In his main project, multiple angiograms with motion artifact will be coupled with pre-angiographic 3D scans to attempt a global alignment and reconstruction. In the first phase, angiograms were registered – frame by frame – to the appropriate x-ray projection through a prior scan and in the second phase, the angiogram will be digitally reconstructed and resampled to provide eradication of all motion. Dr. Abumoussa will also complete multiple professional courses for AI as well as work towards achieving certification as a Google Tensor Flow Engineer.

Daniel Sexton, MD

Duke University

Dr. Sexton used machine learning techniques to complete two projects using electrophysiological (EEG) data. For his main project, he collected and analyzed intracranial EEG data from patients with medication refractory epilepsy. He was able to use a novel interpretable technique developed at Duke to identify network components involved in the period following seizures. For his other project, he used EEG data from a mouse model of traumatic brain injury to determine injury severity. Both projects were accepted for poster presentations at this the CNS Annual Meeting with manuscripts in preparation. Dr. Sexton also completed two Stanford Professional classes for AI specialization and submitted a review article to Neurosurgical Focus on resting state connectivity.

Matthew Pease, MD

Fellow in Neurosurgical Oncology, Memorial Sloan Kettering

Dr. Pease used machine learning techniques to complete two projects related to imaging analysis during his time as the CNS data science fellow. For his main project, he led a multi-institutional, radiomics project where he successfully differentiated between solitary primary CNS lymphoma, glioblastoma, and metastatic disease based on pre-operative MRI. His model, built with nearly 1000 patients, achieved an AUC of 0.95, was accepted for an oral presentation at CNS 2021, and the manuscript is under review. Dr. Pease also completed a project using deep learning to prognose long- term outcomes in severe TBI based on admission clinical information and CT head scans. This project served as the basis for an application for an R01 and was published in Radiology.

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CNS Innovation Institute

Data Science Scholar Program

Goals & Objectives

Data Science Scholar Program Awardees

The CNS Mission

Enhancing health and improving lives through the advancement of neurosurgical education and scientific exchange

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Associated CNS Websites

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CNS Innovation Institute

Data Science Programs

Data Science Scholar Program

Goals & Objectives

Data Science Scholar Program Awardees

The CNS Mission

Enhancing health and improving lives through the advancement of neurosurgical education and scientific exchange

Get Involved

Associated CNS Websites

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Find answers

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