Multicentre Clinical Epidemiological Study of Female Breast Cancer in a Multi-ethnic Malaysian Setting (Epi-Can)

NMRR ID-22-01338-NBF

Epi-Can is a multicentre retrospective cohort study that involves seven of the Ministry of Health (MOH) hospitals in Malaysia. This study aimed to determine the nature of breast cancer patients’ presentation, diagnosis, management of treatment, treatment-induced cardiovascular complications and survival while describing the variation across age, socioeconomic, geographic regions, ethnic differences and its trend over time in Malaysia. The results of this study will aid in observing secular changes as well as long-term outcomes for breast cancer patients in real-world clinical practice.

Contact: Shridevi Subramaniam
shridevi@crc.gov.my 

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.

Case-based Safety Monitoring of Adverse Events Following COVID-19 Vaccination (SAFECOVAC)

NMRR-21-822-59745

SAFECOVAC is an observational study of active safety surveillance for COVID-19 vaccines in Malaysia. The study team evaluates the risk of serious adverse events associated with the administration of COVID-19 vaccines among the vaccinated population. Findings from this study contributes to the local and global safety data of COVID-19 vaccines for recommendation on the COVID-19 immunisation programme.

Contact: Norazida Ab Rahman
norazida@moh.gov.my