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

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.

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

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

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.

The Future of Rehabilitation Robotics: A Framework for Human-Robot Interaction for Complete Training and Assessment

Nguyen Vu Linh | Tran Trung Dung | Ho Ngoc Minh | Yih-Kuen Jan | Manuel Enrique Hernandez

This research project will explore cutting-edge technologies in robotics, physical human-robot interaction, artificial intelligence (AI), and rehabilitation engineering to develop a novel exoskeleton robotic system to achieve complete training. This research will clarify the up-to-date design philosophy, redefine the definition of robots and intelligence means for multistage rehabilitation, and assess physical human-robot interaction to offer safe, robust, and reliable training with shared control (user preference and clinician prescription). If successful, the invented technologies will benefit the healthcare and robotics industries, transforming multistage rehabilitation from therapist-guided to robot-guided treatment.

Accelerating Patient Rehabilitation via Wearable Filament Sensor Networks

This project introduces a low-cost hydraulic soft filament sensor (SFS) integrated into a flexible brace, aimed at monitoring joint angles and guiding users through rehabilitation via a video interface. It incorporates an intelligent calibration technique leveraging a neural network (NN) model, wireless technology, onboard signal processing, a smartphone app, and assessment algorithms using machine-learning models tailored for knee and elbow injuries. This advancement improves the interaction between humans and machines during rehabilitation. Human-subject experiments at Vinmec Times City International Hospital will validate the system’s effectiveness, marking a pioneering effort in physical medicine and rehabilitation at Vinmec and Vinuniversity.

From Causal Understanding of Healthy Longevity to Smart Health

The project has several intertwined methodological research thrusts, including (1) Causal structure discovery to disentangle how various factors interact with one another in the complex system of human health evolution over time, (2) Counterfactual inference to estimate the treatment effect at the individual patient level, thereby allowing personalized decision (treatment), risk assessment, and prevention, (3) Causal evaluation framework to determine how to evaluate the performance or generalization of a causal model, and (4) Causal intervention optimization where adjustable properties of interventions, such as thresholds for action, can be optimized based on their causal impacts. These methodological thrusts will be brought together for cross-cutting healthy longevity applications. Bringing thrusts and cross-cuts together will provide significant insight and design principles into the causal structure of healthy longevity by advancing causality methodology and applying it to large-scale longitudinal health data.

Wastewater Epidemiological Surveillance in Vietnam

While the pandemic is global, transmission is local. Surveillance should be designed based on local context. Besides COVID-19, antimicrobial resistance (AMR) is a silent pandemic, and it only gets worse, as predicted by WHO. The identification of the sentinel sites and the frequency of sampling depends on the surveillance goals. Actionable public health goals in a crisis situation requires a different approach from those for endemic conditions.

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

Kok-Seng Wong | Bo Li | Khoa Doan

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.

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

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.

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

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.

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

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.

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

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.

Smart Indoor Air Quality Control System for Healthier and Greener Buildings

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

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.

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

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

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.

The Future of Rehabilitation Robotics: A Framework for Human-Robot Interaction for Complete Training and Assessment

Nguyen Vu Linh | Tran Trung Dung | Ho Ngoc Minh | Yih-Kuen Jan | Manuel Enrique Hernandez

This research project will explore cutting-edge technologies in robotics, physical human-robot interaction, artificial intelligence (AI), and rehabilitation engineering to develop a novel exoskeleton robotic system to achieve complete training. This research will clarify the up-to-date design philosophy, redefine the definition of robots and intelligence means for multistage rehabilitation, and assess physical human-robot interaction to offer safe, robust, and reliable training with shared control (user preference and clinician prescription). If successful, the invented technologies will benefit the healthcare and robotics industries, transforming multistage rehabilitation from therapist-guided to robot-guided treatment.

Accelerating Patient Rehabilitation via Wearable Filament Sensor Networks

This project introduces a low-cost hydraulic soft filament sensor (SFS) integrated into a flexible brace, aimed at monitoring joint angles and guiding users through rehabilitation via a video interface. It incorporates an intelligent calibration technique leveraging a neural network (NN) model, wireless technology, onboard signal processing, a smartphone app, and assessment algorithms using machine-learning models tailored for knee and elbow injuries. This advancement improves the interaction between humans and machines during rehabilitation. Human-subject experiments at Vinmec Times City International Hospital will validate the system’s effectiveness, marking a pioneering effort in physical medicine and rehabilitation at Vinmec and Vinuniversity.

From Causal Understanding of Healthy Longevity to Smart Health

The project has several intertwined methodological research thrusts, including (1) Causal structure discovery to disentangle how various factors interact with one another in the complex system of human health evolution over time, (2) Counterfactual inference to estimate the treatment effect at the individual patient level, thereby allowing personalized decision (treatment), risk assessment, and prevention, (3) Causal evaluation framework to determine how to evaluate the performance or generalization of a causal model, and (4) Causal intervention optimization where adjustable properties of interventions, such as thresholds for action, can be optimized based on their causal impacts. These methodological thrusts will be brought together for cross-cutting healthy longevity applications. Bringing thrusts and cross-cuts together will provide significant insight and design principles into the causal structure of healthy longevity by advancing causality methodology and applying it to large-scale longitudinal health data.

Wastewater Epidemiological Surveillance in Vietnam

While the pandemic is global, transmission is local. Surveillance should be designed based on local context. Besides COVID-19, antimicrobial resistance (AMR) is a silent pandemic, and it only gets worse, as predicted by WHO. The identification of the sentinel sites and the frequency of sampling depends on the surveillance goals. Actionable public health goals in a crisis situation requires a different approach from those for endemic conditions.

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

Kok-Seng Wong | Bo Li | Khoa Doan

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.

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

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.

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

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.

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

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.

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

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.

Smart Indoor Air Quality Control System for Healthier and Greener Buildings

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

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.

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

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

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.