2020 Jump ARCHES Grant Recipients Announced
Fourteen research projects are sharing $1.9 million in funding through the Jump ARCHES research and development program. The Jump Applied Research for Community Health through Engineering and Simulation (Jump ARCHES), is a partnership between OSF HealthCare and the University of Illinois at Urbana-Champaign. The ARCHES program supports research involving clinicians, engineers, and social scientists to develop technologies and devices that could revolutionize medical training and health care delivery. Faculty at the U of I College of Medicine at Peoria (UICOMP) also participate. Since its inception in 2014, the Jump ARCHES initiative has directed more than $3.7 million for 39 projects. The 14 new awards for 2020, range from $50,000 to $75,000, and include:
A Human Factors Approach to Food Security
Dr. Sarah Stewart de Ramirez-OSF HealthCare and Abigail R. Wooldridge-U of I’s Grainger College of Engineering
Thirty-seven million Americans have food insecurity which results in poor health and increases health care costs. This project will use a human-centered approach to identify barriers for individuals who are food insecure and challenges for service providers who are trying to meet needs in rural communities. That research will support design of technology-bases solutions to reduce food insecurity in rural areas.
A-Eye: Automated Retinopathy of Prematurity Detection and Analysis
Dr. C. Reddy-OSF HealthCare and Thomas Huang-U of I’s Beckman Institute for Advanced Science and Technology
Early detection of retinopathy in premature infants is important for early interventions to prevent blindness. With a shortage of specialists, it’s critically important to develop an AI diagnostic system that autonomously analyzes images of the retina to detect retinopathy. The team will also consider how to integrate the tool into portable, user-friendly equipment with the possibility future expanded uses for such a medical device.
Activate Capture and Digital Counting (AC+DC) Technology for Ultrasensitive and Rapid Characterization of miRNA Blood borne Biomarkers for ALS
Dr. Vahid Tohidi-OSF HealthCare and Brian Cunningham-U of I’s Grainger College of Engineering
ALS is a devastating condition that leads to gradual muscle decline caused by loss of motor neurons in the brain and spinal cord. It’s in urgent need of new treatments. The goal of this proposal is to develop and validate nanoparticle technology that can use a small amount of blood plasma to identify miRNA biomarkers of ALS. The team will also develop an instrument using just a drop of blood to detect statistically significant circulating biomarkers to identify genetic indicators of ALS.
Artificial intelligence augmented portable photoacoustic imaging system for early diagnosis of breast cancer
Dr. Kent Hoskins-OSF HealthCare and Yun-Sheng Chen-U of I’s Beckman Institute for Advanced Science and Technology
This research aims to harness artificial intelligence (AI) to develop an affordable, portable imaging solution for breast cancer screening and diagnosis that could be more accessible to residents in rural communities. The team proposing using Photoacoustic (PA) imaging techniques that combine optical (photo) and ultrasound (acoustic) approaches to produce high-contrast, molecular images of breast blood vessel and lymphatic systems for early breast cancer diagnosis.
Autonomous Morphing Bed Mattress for ALS patients with Limited Movement Ability
Dr. Christopher Zallek-OSF Healthcare/U of I College of Medicine, Peoria and Elizabeth Hsiao-Wecksler - U of I's Grainger College of Engineering
This project will address complications from limited to no movement ability of adults while lying in bed, including patients with ALS who have weak muscles and loss of ability to control them. The team will develop an innovative bed mattress consisting of an array of soft air cells that will autonomously pressurize and depressurize specific areas to provide site-specific pressure relief, tilted repositioning, and assistance with transferring while the patient is lying flat or has their head elevated.
Automated Aneurysm Segmentation and Measurement
Dr. Jeff Klopfenstein-OSF HealthCare and Thomas Huang-U of I’s Beckman Institute for Advanced Science and Technology
Cerebral aneurysms are among the most deadly types. This group will build a large-scale dataset to create an algorithm to identify and segment the bulging blood vessels based on size and blood flow. This will be used for future medical imaging instruction and to develop computer programs to help with treatment decisions.
Design and Validation of a Soft Robotic Cardiac Transseptal Puncture Simulator
Dr. Abraham Kocheril-OSF HealthCare and Girish Krishnan-U of I’s Grainger College of Engineering
This project continues work on a realistic soft heart simulator that allows early-career cardiologists and surgeons to feel what it’s like to poke and prod cardiac tissues during a common surgery for patients with an irregular heartbeat. Phase II will enhance the level of realism by fine- tuning the materials used and incorporating image-based guidance.
Development of a Digital Fall Risk Assessment and Prevention Tool for Rural Older Adults
Dr. Sarah Stewart de Ramirez-OSF HealthCare and Jacob Sosnoff-U of I’s Beckman Institute for Advanced Science and Engineering
Falls are the number one cause of accidental injury in older adults. This project will use a machine learning algorithm for a fall risk assessment and prevention strategy application as part of a community health worker's digital toolkit. Researchers will also assess the usability of the “Steady” tool.
Digitizing the Neurological Screening Examination
Dr. Christopher Zallek-OSF HealthCare/U of I College of Medicine, Peoria-UNICOMP and George Heintz-U of I’s Health Care Engineering Systems Center
There’s a projected 19% shortage of neurologists nationally by 2025 and yet nine percent of primary care visits are with patients who have neurological issues. This project will pilot an integrated Digital Neurological Examination (DNE) system and develop a platform using data for an AI-informed decision support assistant. The assistant will help physicians triage and care for patients with neurological symptoms regardless of exam location.
Improving Feedback and Efficiency: Automated Grading of Post Simulation Written Chart Notes
Dr. William Bond-OSF HealthCare and Suma Bhat-U of I’s Grainger College of Engineering
Immediate feedback fosters the best learning and this project aims to improve Automated Short Answer Grading (ASAG) using Natural Language Processing (NLP) methods from previously collected and graded chart notes following simulations using standard participants (actor-based simulations). The tools developed will also reduce faculty grading demands and can be applied to trainings for other topics including use of opiates, telehealth use, patient counseling.
Improving Outcomes and Training of Pectus Excavatum
Dr. Paul Jeziorczak-OSF HealthCare and Inki Kim-U of I’s Grainger College of Engineering
This team will develop a process using virtual and augmented reality to improve patient education, resident training, and placement of an internal metal chest brace for patients with pectus excavatum or sunken chest which can impact the function of the heart and lungs. The team will build on work already done with pediatric hearts and build a training model using 3D printed chest walls as well as a virtual reality module for self-study as well as pre-operative planning.
Optimizing Deployment of Community Health Workers
Dr. Sarah Stewart de Ramirez-OSF HealthCare and Hyojung Kang-U of I’s College of Applied Health Sciences
Community Health Workers are effective for improving health and lowering healthcare costs for vulnerable populations, such as those living in rural areas where access to healthcare is limited and health outcomes are poor. The project will create data-driven algorithms to support optimal deployment of precision guided, digitally enabled CHWs in rural settings.
Skill Assessment in Surgery and Microsurgery
Dr. Heidi Phillips-U of I’s College of Veterinary Medicine and T. Kesavadas-U of I’s Health Care Engineering Systems Center
We propose applying advanced engineering and data science to develop a high-fidelity virtual simulator to provide thorough and validated microsurgical training and assessment. The team will develop an evidence-supported, automated, robust, real-time, comprehensive, quantitative (ARRCQ) assessment system by building data sets and creating algorithms for optimum learning including accuracy and cost.
Virtual Reality to Deliver Psychotherapy to Lung Cancer Patients with Depression
Dr. Rhonda L. Johnson-OSF HealthCare and Rosalba Hernandez-U of I’s School of Social Work
More than half of all lung patients experience depression which impacts their compliance with treatment, increases hospitalization and ultimately decreases survival rates. With a shortage of psychotherapists across the country, especially in rural areas, this project’s virtual reality (VR) platform could fill the void. For example, VR programs could transport users to relaxing environments with guided meditation. If successful, this treatment could be used as patients receive chemotherapy or before/after radiation.