CODIQ-My 2.0

IoT

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REAL WORLD APPLICATION OF A DIGITAL HEALTH SOLUTION: COGNITIVE OBSERVATIONAL DIAGNOSIS FOR QUANTIFIED HOME MONITORING (CODIQ - My) 

 

NMRR ID-22-01042-SDM

 

The COVID-19 pandemic has highlighted vulnerabilities in healthcare systems, with profound implications for public health, economic progress, and social cohesion. In response to this crisis, there is a growing demand for large-scale population screening and monitoring, and digital health solutions, particularly remote monitoring, have emerged as valuable tools to address this need. Remote monitoring offers distinct operational and design features well-suited to the challenges posed by not only COVID-19 but also but any other medical emergencies, including asynchronous communication and real-time clinical data collection.

Prior evidence underscores the predictive value of monitoring critical indicators such as oxygen saturation, respiratory rate, and fever in assessing the progression of COVID-19. Leveraging cutting-edge technology, including mobile applications and biosensors, remote monitoring empowers healthcare professionals to oversee patients' health remotely and in real-time, right from the comfort of their homes.

This ongoing study builds upon the foundation established by their earlier project initiated in early 2021, titled "Asymptomatic COVID-19 Quarantine Digital Solution: A Proof of Concept Study." In that endeavor, it was  successfully demonstrated the feasibility of a prototype or beta version, providing a secure virtual home quarantine environment. Now, the current study aims to evaluate the scalability and feasibility of implementing this solution on a broader scale across all infectious diseases.

CODIQ-My, harnesses the potential of the Internet of Medical Things (IoMT), combining biosensor technology with a user-friendly mobile application. Additionally, it incorporates a web-based platform and an operational dashboard equipped with active alerts, enabling healthcare authorities to continuously monitor patients in real-time. CODIQ-My is designed to seamlessly integrate with any validated biosensor available in the market, ensuring adaptability and versatility. Furthermore, utilizing a validated risk predictor to promptly identify high-risk patients, facilitating swift and decisive action by healthcare authorities.

This ongoing project leverages on the 4th industrial revolution  to transform remote monitoring and support for patients nationwide. This project aims  to contribute to a safer, more efficient, and responsive health care system, ultimately safeguarding the well-being of communities.

 

 

Lepre.My

IoT

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DEVELOPMENT AND VALIDATION OF A SKIN MOBILE APPLICATION USING ARTIFICIAL INTELLIGENCE (A.I.) FOR SCREENING OF LEPROSY IN MALAYSIAN POPULATION 

 

NMRR ID-22-02743-GCO

 

Leprosy, a persistent infectious condition caused by Mycobacterium leprae, often remains hidden in our bodies for years, with an average incubation period of about 5 years and the potential to stay dormant for up to 20 years before revealing itself through visible symptoms.

Malaysia has achieved the remarkable status of leprosy elimination since 1994, boasting a prevalence rate of just 0.9 cases per 10,000 people. However, we still face challenges in detecting cases in a timely manner. Surprisingly, over 80% of our cases are the more severe multi-bacillary type of leprosy, and 5%-6% of new cases already exhibit permanent disabilities. Shockingly, more than 3% of these cases are children.

One significant hurdle to early detection is the low level of suspicion among our medical professionals. Awareness about leprosy and its telltale signs remains alarmingly inadequate. Often this disease is misdiagnosed as common skin ailments like contact dermatitis, psoriasis, eczema, vitiligo, or a simple skin infection.

With advances in deep learning and machine learning, particularly through convolutional neural networks (CNN), have revolutionized medical image analysis. In dermatology, these cutting-edge techniques have achieved astonishing accuracy rates of over 90% in diagnosing various skin conditions.

Our groundbreaking study aims to harness the power of AI-driven digital health screening using images to combat leprosy effectively, aligning with the WHO's global strategy to eliminate leprosy entirely, moving us closer to "Towards Zero Leprosy." This innovative concept expands access to timely patient care and accelerates treatment for those diagnosed positively, ensuring a brighter, healthier future for all.

 

 

BREATH

IoT

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DEVELOPMENT OF BREATHING FLOW RATES AS A BIOPHYSICAL MARKER AND AI-ASSISTED BREATHING SENSOR FOR THE DETECTION OF ASTHMA AND CHRONIC OBSTRUCTIVE AIRWAY DISEASE 

 

NMRR ID-23-01015-XQ2

 

Changes in human breathing pattern and rate are clinical manifestations of many diseases involving respiratory changes and failure, including asthma, anaemia, septic shock, anxiety attacks, pulmonary embolisms, heart attacks, emphysema, pneumonia, hypothyroidism and so on. Current methods of detecting changes in breathing patterns are very limited and cumbersome in clinical settings. For instance, spirometry can be used to measure lung function and volume at a single point in time only, therefore its use in inpatient setting for continuous assessment is questionable. Plethysmography is bulky and requires patients to perform the test in a chamber with specific steps to follow for accurate results.

 

 

CODIQ-My

IoT

CODIQ My

 

ASYMPTOMATIC COVID-19 QUARANTINE DIGITAL SOLUTION: A PROOF OF CONCEPT STUDY

 

NMRR-20-2761-57684

 

The DHRi team along several collaborators has developed a home monitoring solution utilizing the internet of things (IoT) and machine learning for the purpose of monitoring COVID-19 home quarantined patients besides its ability to geofence. This study aims to identify the feasibility of this solution in its application.

 

Publication: 1

 

Report: 1