In case-based simulations for extracorporeal membrane oxygenation (ECMO), for example, trainees perform tasks such as cannulation while instructors observe, guide, and correct. The success of surgical training relies on the close connection between instructors and trainees. The development of such technology to localize and image the epileptogenic zone in drug resistant epilepsy patients will benefit numerous patients and the healthcare system. The successful completion of the proposed research promises to lead to establishment of a disruptive technology, which can significantly advance state-of-the-art management of intractable epilepsy, by means of an innovative ML approach. The goal of this proposed research project is to develop a novel machine learning (ML)-based technology that can image and localize epileptogenic brain from noninvasive magnetoencephalography (MEG) measurements to aid surgical planning in patients with drug resistant epilepsy.ĭetermining the epileptogenic brain is of crucial significance for the successful surgical treatment of intractable epilepsy. Researchers for this project includes Bin He, PhD, Trustee Professor of Biomedical Engineering at Carnegie Mellon University and Anto Bagic, MD, Professor of Neurology and Chief of Epilepsy Division at the University of Pittsburgh Medical School. These results highlight the need for improved diagnostic tools and therapeutic options for drug resistant epilepsy. Unfortunately, many patients are not candidates for epilepsy surgery because the brain region responsible for generating seizures cannot be localized and mapped, or seizures originate from the eloquent cortex that cannot be removed without neurological deficits. Epilepsy surgery has the best chance of curing epilepsy but is only an option if the brain region generating seizures can be accurately localized and safely removed. Currently, treatment options for these patients are limited to epilepsy surgery, vagus nerve stimulation, or enrollment in experimental protocols, such as brain stimulation. Focal epilepsy, which are seizures that begin in a focal region of the brain, represents the most common type of drug resistant epilepsy. ![]() For approximately 30% of the 3.4 million Americans with epilepsy, the seizures are not controlled by medical therapy alone. All proposals should be submitted via email to: SummaryĮpilepsy affects over 65 million people worldwide. Proposal submissions are open only to Carnegie Mellon faculty in all units of the university. Once selected for an award, the CMLH and UPMC Enterprises will help identify potential clinical and data-transaction partners and provide guidance related to commercialization activities, as needed. ![]() In addition to offering funding, CMLH provides support for funded projects in the form of datasets, access to patients and doctors for empirical validation of new concepts, and entrepreneurial mentorship.Īwards are intended to support research that transforms healthcare from transactional and experience-centered to data-driven. After one year, projects may attract more funding to refine the technology and/or its development for commercialization. We welcome proposals that involve human-computer interaction, language technologies, information systems, computer graphics, computer vision, artificial intelligence, robotics, electrical engineering, economics, psychology, sociology, public policy, business administration, law, design, and any other disciplines that apply to healthcare.ĬMLH initially provides research projects with approximately one year of funding. Although many CMLH projects will involve data analytics and machine learning, our approach is technology agnostic. All funded work at CMLH will have a clear line of sight to commercial application. From bench to bedside: the CMLH funds projects that strive to bridge the gap between research and practice.
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