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

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

Principal Investigators & Key Members:
Minh Do | Pham Huy Hieu | 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.
Privacy-Preserving, Robust, and Explainable Federated Learning Framework for Healthcare System

Privacy-Preserving, Robust, and Explainable Federated Learning Framework for Healthcare System

Principal Investigators & Key Members:
Bo Li | Kok-Seng Wong | Dam Thuy Trang | Luu Hong Nhung
The project focuses on designing a trustworthy federated learning (FL) framework for the healthcare system with theoretical guarantees for its privacy, robustness, and agent-level data valuation and explainability, aiming to make the healthcare systems more efficient and trustworthy.
Wastewater Epidemiological Surveillance in Vietnam

Wastewater Epidemiological Surveillance in Vietnam

Principal Investigators & Key Members:
Helen Nguyen | Minh Do | Ahmed Elbanna | Andrew Taylor-Robinson | Maurizio Trevisan | Nguyen Xuan Hung | Le Cu Linh | Huynh Dinh Chien
The project aims to determine the prevalence of SARS-CoV2 in the selected communities. The project findings will give valuable insights into understanding the evolution of antimicrobial resistance in response to human behavior and community practices, and medical practices. The project will also develop a non-invasive, privacy-preserved tool for epidemiological surveillance for pathogen transmission in local communities.
Point of Care and Telehealth Diagnostics for Data-Driven Smart Health Systems

Point of Care and Telehealth Diagnostics for Data-Driven Smart Health Systems

Principal Investigators & Key Members:
Brian Cunningham | Xing Wang | Quynh Le | Thanh Ngoc Tien | Cuong Do Danh
This project aims to assemble a multidisciplinary collaboration with the goal of developing, demonstrating, and characterizing point of care and self-testing diagnostic technologies that take advantage of the unique properties of photonic metamaterials, MEMS sensors, and molecular biology methods using engineered nucleic acid probes. Our project will pave the way toward a substantially more robust and high-quality collection of biomarker data that, when integrated with a telehealth service system, will form the basis of mass-market products and services for health management.
Development of Point-Of-Care Devices to Predict Dengue Infection Status and to Detect Sepsis Biomarkers Principal

Development of Point-Of-Care Devices to Predict Dengue Infection Status and to Detect Sepsis Biomarkers Principal

Principal Investigators & Key Members:
Rashid Bashir | Andrew Taylor-Robinson | Minh Do | Phung Nam Lam
Develop point-of-care microfluidic approaches to detect multiple dengue or sepsis biomarkers (nucleic acids, cells, and proteins) from the same sample of whole blood. POC testing of these dengue and sepsis biomarkers could accelerate the clinical decision for early detection of dengue and sepsis, respectively. Importantly, the project plans to demonstrate approaches as global health solutions to make our technologies achievable to historically underserved populations in Vietnam and other low-income countries by reducing the existing gaps of required infrastructure and high cost.