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Center Overview
MIXR supports the development and scientific foundations for designing and assessing extended-reality (XR) applications in medicine. Collaborating with our industry partners and regulatory scientists, we are exploring the use of XR to improve public health and healthcare outcomes across education and simulation, clinical care delivery, and applying data science and machine learning to improve diagnosis and therapeutics.
MIXR has specifically identified driving applications in clinical practice, medical education and training, and regulatory standards. In service of these, we bring unique capabilities in scene acquisition, predictive modeling, immersive telepresence, human factors, and imaging and displays.
Universities
- University of Michigan
- University of Maryland, College Park
- University of Maryland, Baltimore
Center Personnel
Amitabh Varshney
Center Director, University of Maryland, College Park
+1 301 405 2316
varshney@umd.edu
Sarah B Murthi
Site Director, University of Maryland, Baltimore
+1 410 328 6704
smurthi@umm.edu
Rishindra Reddy
Site Director, University of Michigan
+1 734 936 5738
reddyrm@med.umich.edu
Mark S Cohen
Dean, Carle Illinois College of Medicine
+1 217 300 5700
meddean@illinois.edu
Research Focus
MIXR has specifically identified driving applications in clinical practice, medical education and training, and regulatory standards. In service of these, we bring unique capabilities in scene acquisition, predictive modeling, immersive telepresence, human factors, and imaging and displays. Beyond these applications, we are interested in evaluating metrics and usability, 5G/6G, artificial intelligence (AI) and machine learning (ML), and displays for XR devices in healthcare applications. Working with the U.S. Food and Drug Administration (FDA) and other regulatory and standards agencies, we address open research questions in these areas. Program outcomes will provide the scientific foundation in support of regulatory requirements and decisions regarding XR devices; our focus will be on current gaps and evaluation challenges across a range of clinical specialties and various XR hardware and software platforms. Bridging the gaps currently preventing the ubiquitous use of XR could decrease medical errors and save lives, and thus have a substantial economic impact on medicine.
Awards
MIXR has identified driving applications in clinical practice, medical education and training, and regulatory standards. The University of Maryland, Baltimore (UMB) contributes to the Center with unique capabilities in: predictive modeling (AI-assisted methods to identify anomalies in medical data), human factors (pilot and field evaluation of XR solutions), and imaging and displays (conceptual design and iterative user studies for prototype XR tools in medical environments). The team at UMB will work from the R Adams Cowley Shock Trauma Center at the University of Maryland School of Medicine focusing on XR in trauma, surgery, and critical care applications.
XR device evaluation lags significantly behind technological innovation, increasing the risk of ineffective or unsafe applications and delaying clinical implementation. This effort’s broader impact will be addressing open research questions for the evaluation of medical XR devices. Supporting NSF’s mission, this will assure public safety around these technologies and improve health care outcomes. In collaboration with the U.S. Food and Drug Administration, project outcomes will provide the scientific foundation supporting regulatory requirements on and decisions regarding medical XR devices. Further, MIXR’s multi-faceted strategic approach for creating educational opportunities for underrepresented students focuses on access, community, and innovation.
Data, software, findings, and publications resulting from this project will be archived across the sites, but with a central server at http://mixr.umd.edu to facilitate ease of discovery. Research and archival staff will review the data collected during this project before release to the public. It shall be publicly available for at least three years after the project.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. Read More >
MIXR has identified driving applications in clinical practice, education, and regulatory standards for XR. The University of Michigan will focus on unmet research needs related to 1) education and simulation (using mixed and virtual reality headsets) 2) telemedicine (using mixed reality headsets and smart glasses over 5G/6G network); and 3) artificial intelligence and deep machine learning methods for clinical decision making. Quantitative and qualitative analysis will be used to evaluate new technologies, while research outcomes will provide the scientific foundation in support of regulatory requirements, evaluation, and implementation of XR devices across a range of clinical and educational applications.
XR device evaluation lags significantly behind technological innovation, increasing the risk of ineffective or unsafe applications and delaying clinical implementation. This effort’s broader impact will be addressing open research questions for the evaluation of medical XR devices. Supporting NSF’s mission, this will assure public safety around these technologies and improve health care outcomes. In collaboration with the U.S. Food and Drug Administration, project outcomes will provide the scientific foundation supporting regulatory requirements on and decisions regarding medical XR devices. Further, MIXR’s multi-faceted strategic approach for creating educational opportunities for underrepresented students focuses on access, community, and innovation.
Data, software, findings, and publications resulting from this project will be archived across the sites, but with a central server at http://mixr.umd.edu to facilitate ease of discovery. Research and archival staff will review the data collected during this project before release to the public. It shall be publicly available for at least three years after the project.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. Read More >
MIXR has identified driving applications in clinical practice, medical education and training, and regulatory standards. The University of Maryland, College Park leads the Center with unique capabilities in: medical scenario acquisition (a state-of-the-art multi-camera array and a software pipeline for acquiring and fusing visual data), predictive modeling (AI-assisted methods to identify anomalies in medical data), immersive telepresence (high-resolution, high-frame-rate dynamic light field rendering to facilitate medical image transmission using low-latency, high-precision representations), human factors (immersive presence interfaces), and imaging and displays (bench-testing methods evaluating XR modalities in medical environments).
XR device evaluation lags significantly behind technological innovation, increasing the risk of ineffective or unsafe applications and delaying clinical implementation. This effort’s broader impact will be addressing open research questions for the evaluation of medical XR devices. Supporting NSF’s mission, this will assure public safety around these technologies and improve health care outcomes. In collaboration with the U.S. Food and Drug Administration, project outcomes will provide the scientific foundation supporting regulatory requirements on and decisions regarding medical XR devices. Further, MIXR’s multi-faceted strategic approach for creating educational opportunities for underrepresented students focuses on access, community, and innovation.
Data, software, findings, and publications resulting from this project will be archived across the sites, but with a central server at http://mixr.umd.edu to facilitate ease of discovery. Research and archival staff will review the data collected during this project before release to the public. It shall be publicly available for at least three years after the project.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. Read More >