Carle Illinois Faculty R&D Efforts Advance Diagnosis/Health Care Training with New Funding
Carle Illinois College of Medicine faculty members working to create technologies and devices to advance health care delivery and medical training are sharing in nearly $800,000 in grants from the Jump ARCHES (Applied Research in Community Health through Engineering and Simulation) research and development program. Eleven cross-disciplinary teams – including seven with faculty from Carle Illinois – are sharing in grants announced for Spring of 2022.
The latest round of grants targeted solutions that promote recovery post-COVID-19 or similar health crises, improve health care and health literacy among historically underserved populations through improved diagnosis and treatment, improve patient interaction by reducing administrative burden at the bedside, improve diagnosis and treatment of neurological disorders, and incorporate personalized precision medicine and genomic best practices to address evolving standards of care.
Teams applying for grants were required to focus on solutions that could be deployed within four to six weeks. Researchers were also encouraged to consider how to best mitigate the impact of age, location, and social barriers in delivering quality health care to vulnerable populations. Brad Sutton, one of Carle Illinois’ professors, is on two of the research teams receiving funding.
The Jump ARCHES endowment program is a partnership between OSF HealthCare and the University of Illinois Urbana-Champaign (U of I) and its College of Medicine in Peoria (UICOMP). Grants support research involving clinicians, engineers, and social scientists to rapidly develop technologies and devices that could revolutionize medical training and health care delivery.
Carle Illinois faculty are working on the following projects funded in the Spring 2022 grant cycle:
- Koopman framework for detecting mental health changes in multimodal wearable data
Team members: Manuel E. Hernandez, Ph.D., College of Applied Health Sciences, Assistant Professor of Biomedical and Translational Sciences (Carle Illinois College of Medicine), UIUC; Jean Clore, Ph.D., UIUC; Richard Sowers, Ph.D., Professor of Engineering, Mathematics, and Biomedical and Translational Sciences (Carle Illinois College of Medicine), UIUC; Elizabeth Hsiao-Wecksler, Ph.D., Professor of Mechanical Science and Engineering, Bioengineering, and Biomedical and Translational Sciences (Carle Illinois College of Medicine), UIUC.
This interdisciplinary project will combine development of a machine learning/artificial intelligence framework for detecting and predicting short-term and long-term changes in anxiety state using multimodal sensors to collect electrophysiological, acoustic, and/or kinematic measurements, and well-established psychosocial paradigms. The goal is to intuitively and intelligently collect, sense, connect, analyze, and interpret data from sensor systems to enable discovery of mental health symptoms, such as anxiety, and optimize health in adults.
- Advanced Auscultation Audio Algorithmic Analysis (a5)
Team members: Adam Cross, M.D., FAAP, Clinical Research Informaticist, OSF HealthCare; Jennifer Amos, Ph.D., Teaching Professor Biomedical and Translational Sciences (Carle Illinois College of Medicine), Bioengineering; and Eliot Bethke, Instructor (Carle Illinois College of Medicine), UIUC.
The project focuses on advanced feature extraction and processing to improve analytical performance to enable end-to-end explainable output and avoid the “black box” problem so prevalent among current models for analyzing lung sounds. The project will use the publicly available lung audio recordings and raters trained in auscultation to label adventitious lung sounds, and then use this data to build and test the algorithm.
- Toward Machine Learned Aortic Arch Measured Diameters
Team members: Matthew Bramlet, M.D., Department of Pediatrics UICOMP, Advanced Imaging and Modeling Lab, Jump Simulation; Brad Sutton, Ph.D., Professor of Biomedical and Translational Sciences (Carle Illinois College of Medicine), Bioengineering and Engineering, UIUC.
Current methods of generating pediatric normative data are cumbersome and time consuming, and automated methods of generating normative data have not yet been developed. This project seeks to create a new tool that allows each institution to generate their own normative data which is relevant to their own institution, modality, and methodology. In doing so, it will create confidence in clinical decisions based on local measures and experience.
- Development of a pneumothorax computational model toward lung metastasis visualization and modeling
Team members: Matthew Bramlet, M.D., Department of Pediatrics UICOMP; Advanced Imaging and Modeling Lab, Jump Simulation; Brad Sutton, Ph.D., Professor of Biomedical and Translational Sciences (Carle Illinois College of Medicine), Bioengineering and Engineering, UIUC; Dan Robertson, M.D., OSF HealthCare; Alexa Waltz, OSF HealthCare; Olivia Bryan, OSF HealthCare.
This project seeks to build on the utility of virtual reality-based 3D modeling of pre-surgical anatomy by expanding beyond our previous work with congenital heart disease and large tumor resection which have already been fully implemented within the Children’s Hospital of Illinois (CHOI). The core of this project’s impact is the ability to recreate the surgical field in full 3D prior to the actual procedure allowing the surgeon to embed a more accurate mental representation of the surgical field in their mind prior to surgery. This project looks to bridge the gap between existing capability and computational modeling of 3D localization in a deflated lung toward improved 3D mental representation of actual deflated surgical lung lesions.
- Enhanced Focality of Transcranial Magnetic Stimulation Using an Ultrathin Wearable Metasurface for Treating Neurological Disorders
Team members: Yang Zaho, Ph.D., UIUC; Yun-Sheng Chen, Ph.D., Assistant Professor, Biomedical and Translational Sciences (Carle Illinois College of Medicine), Electrical and Computer Engineering, Bioengineering, Beckman Institute for Advanced Science and Technology, UIUC; and Huan Huynh, M.D., OSF HealthCare.
The objective of this proposal is to demonstrate the feasibility of an innovative ultrathin metasurface device that is compatible with Transcranial Magnetic Stimulation to enhance the spatial resolution and control the focal distribution of the stimulating field, incorporating a dual-modal photoacoustic/ultrasound imaging technology that images the brain oxygen saturation and blood flow, which are known to vary during TMS.
- CAnPredict: An Algorithm for Improved Pancreatic Ductal Adenocarcinoma Detection
Team members: Sonia Orcutt, M.D., Department of Surgery, UICOMP; Ravishanakar K. Iyer, Ph.D., Professor of Electrical and Computer Engineering, Computer Science, and Biomedical and Translational Sciences (Carle Illinois College of Medicine), UIUC; Christopher Gondi, Ph.D., Cancer Center at Illinois; Lusine Demirkhanyan, Ph.D., UICOMP; Andrew Darr, Ph.D., UIUC; James Weldy, OSF HealthCare; Nathan Pritzker, M.B.A., OSF HealthCare; Mosbah Aouad, UIUC.
The goal of this proposal is to enhance the early detectability of PDAC (a type of pancreatic cancer) to improve patient survival rate. The interdisciplinary research team will develop a predictive diagnostic algorithm based on existing patient historical, multimodal data. Successful completion of the work will not only be relevant to early pancreatic cancer diagnosis, but also establish new ways to perform diagnostic targeting of several other cryptic disease processes.
- Development of a Chatbot for Delivering Long-Term Motivational Interviewing for Improving Exercise Adherence in Hemodialysis Patients
Team members: Jessie Chin, Ph.D., School of Information Sciences, UIUC; Suma Bhat, UIUC; Ben Pflederer, M.D., OSF HealthCare; Chung-Yi Chiu, Ph.D., UIUC; Ken Wilund, Ph.D., Professor of Kinesiology and Community Health, Biomedical and Translational Sciences (Carle Illinois College of Medicine), UIUC; Rehan Shah, UIUC.
The proposed study will bridge theories in behavioral sciences and Natural Language Processing (NLP) to develop a long-term Motivational Interviewing conversational agent, LogMintBot, aimed at promoting exercise adherence among hemodialysis patients.
Editor’s note: Complete summaries of each project receiving funding can be found here.