Projects - Smarthealthcenter

Developing a unified, low-cost, self-care mobile health application for common disease screening and early detection in low-and middle-income countries

Principal Investigators & Key Members:

This work aims to develop a low-cost, unified machine learning-based screening tool using multimodal signals collected from smartphones and wearable devices to evaluate the risk of presenting with common, high-demanding NCDs (stroke, chronic respiratory diseases, and neurodegenerative diseases) 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.

Evaluating the Effect of Antiviral Drugs using Polarized Light Imaging and Machine Learning Approaches: The Case of Human-induced Pluripotent Stem Cell-derived Cardiomyocytes

Principal Investigators & Key Members:

In this project, we propose to develop a standard and robust procedure to evaluate the effectiveness of antiviral drugs using label-free, noninvasive light imaging, and machine learning-based approaches. To demonstrate this with a representative example, we will start with the evaluation of the effects of Molnupiravir, used for treating SAR-CoV- 2, on cardiomyocytes derived from a human-induced pluripotent stem cell. This project will then be extended to the evaluation of other antiviral drugs.

Detection and quantitation of cancer ctDNA and miRNA for point of care lung cancer therapy selection

Principal Investigators & Key Members:

Brian Cunningham, PhD | Xing Wang, PhD | Nguyen Xuan Hung | Yi Hyeon Gyu | Tran Thi Mai

This project focuses specifically on rigorously demonstrating the performance of a novel assay approach called “Activate, Cleave, Capture, and Count” (AC3) for ultra-sensitive detection and quantification of several well-known mutations with clinical relevance for guiding initial therapy selection. We aim to design, demonstrate, and validate AC3 assays for KRAS mutations in lung, colorectal, and pancreatic cancers.

Envisioning Urban Environments Resilient to Vector-Borne Diseases: A One Health Approach to Dengue Management

Principal Investigators & Key Members:

This project aims to develop a comprehensive modeling framework to predict the risk of Dengue infection in Vietnam. We create digital twins of the urban environment, which receive sensor data for factors influencing Dengue transmissions, such as temperature, humidity, and CO2 concentration. Our model will predict infection risk levels in real-time and make corresponding public health recommendations for risk reduction.

Smart Indoor Air Quality Control System for Healthier and Greener Buildings

Principal Investigators & Key Members:

Vishal Verma | Jinhui Yan | Le Duy Dung | Kok-Seng Wong, PhD | Andrew Taylor-Robinson, PhD

The quality of the indoor environment has a critical impact on people’s health because on average, we spend more than 90% of our time indoors. Providing a healthy and safe indoor environment can save lives, reduce diseases, and increase our quality of life. Better management of indoor environmental quality and saving energy consumption at the same time is of critical national and international significance. This project aims at building a virtual platform that offers interactive interfaces for infection control and facility managers to make informed and optimal intervention strategies as per different intended uses of the multi-used indoor environments.

VAIPE: AI-assisted IoT-enabled smart, optimal, and Protective hEalthcare monitoring and supporting system for Vietnamese

Principal Investigators & Key Members:

Minh Do | Hieu Pham | Thanh Hung Nguyen | Phi Le Nguyen

The VAIPE project aims to develop a smartphone application that uses the camera and novel AI and visual recognition methods to allow the user to easily digitalize and analyze health records, including doctor’s diagnostics and prescription, daily in-take medication, and readings of medical devices at home. The ultimate goal is to provide ordinary citizens with easy access to timely, reliable, usable, and personalized information and intelligence about their health.