Principal Investigators:

Mark Hasegawa-Johnson, Ph.D., The Grainger College of Engineering, Department of Electrical and Computer Engineering, UIUC. Minh Do, Ph.D., The Grainger College of Engineering, Department of Electrical and Computer Engineering, UIUC.
Hieu Pham, Ph.D., College of Engineering and Computer Science and VinUni-Illinois Smart Health Center, VinUniveristy.
Dinh Nguyen, Ph.D., College of Engineering and Computer Science, VinUniveristy.
Huyen Nguyen, RD, Ph.D., College of Health Science, VinUniveristy.
Vo Thanh Nhan, M.D., Ph.D., Director of the Cardiovascular Center, Vinmec Times City International Hospital. Ngoc-Minh Ho, M.D., 3D Motion Lab, Orthopaedic and Sports Medicine Center, Vinmec Times City International Hospital. Nghia Nguyen, Integrated Mental Health Care Center, Vinmec Healthcare System

Motivation: Noncommunicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, and neurodegenerative diseases kill about 41 million people every year and cause the majority of global disease burden and modality, equivalent to 74% of all deaths globally (WHO, 2022). Most of these premature deaths occur in low- and middle-income countries. Among NCDs, heart attacks and strokes account for most NCD deaths, or 17.9 million people annually, followed by chronic respiratory diseases (4.1 million). In reality, NCD diseases are often delayed or misdiagnosed, even undiagnosed (Florencia Luna et al., 2020).

This work aims to develop a low-cost, unified machine learning-based screening tool using multimodal signals collected from smartphones/wearable devices to evaluate the risk of presenting with common, high-demanding NCDs including stroke, chronic respiratory diseases, and neurodegenerative diseases (see Figure 1). We will clinically evaluate the app for common disease screening and early detection in low-and middle-income countries. This tool can be used at home or the point of care and opens up the opportunity to bring digital healthcare solutions to millions of people across countries.

Future Extensions: The success of this study opens new opportunities to use commodity smartphones to build various applications to support complete, accurate, yet reliable health tests in regular home settings. These applications allow measures of multiple health parameters and measurements via visual and acoustics sensing. Clinical studies to see if the tools can be reliable for disease tracking and evaluation out of the clinic. The research team expects to apply for external funding from VinIF or NSF grants to support an extensive clinical validation of the proposed AI systems by comparison against current diagnostic technology. This plan can be conducted at Vinmec Hospital and other major hospitals in Vietnam. We also have a plan to work with our clinical partners in the US through UIUC faculty, thus sustaining this multidisciplinary research collaboration between VinUni and UIUC. An important goal is to demonstrate our AI systems as accurate tools to contribute to the early diagnosis and screening of common non-communicable diseases that can work effectively across countries. Finally, this proposal provides a great opportunity to explore technology transfer activities and anticipate that these devices will contribute to improving the current standards of patient care.