TESTING AND VALIDATION OF MACHINE LEARNING MODEL FOR TUBERCULOSIS SCREENING USING CHEST RADIOGRAPHS IN MALAYSIAN POPULATION
iTBXR, a vital component of the iTB initiatives, builds upon the groundwork laid by the iTB-REPOXR project. It is specifically designed to revolutionize tuberculosis screening using chest radiographs, contributing significantly to the global and national goal of ending TB by 2035.
By capitalizing on the rich iTB-REPOXR database, iTBXR is pioneering Malaysia's first-ever AI-driven solution for TB diagnosis, marking a significant milestone in the nation's fight against this disease.
iTBXR employs advanced AI algorithms and machine learning techniques to identify TB indicators swiftly and accurately within chest X-rays. This approach not only expedites diagnosis but also enhances its precision, ensuring timely and effective treatment for those at risk.
Furthermore, iTBXR aligns with global efforts to combat TB by reducing transmission risks. Prompt detection means that affected individuals can receive timely treatment, ultimately curbing the disease's spread. This initiative complements Sustainable Development Goal 3, which aims to ensure universal health and well-being.
iTBXR's AI-driven TB screening, empowered by the iTB-REPOXR database, represents the nation’s step toward a TB-free world by 2035. It underscores Malaysia's dedication to addressing this global health challenge and exemplifies the transformative potential of AI in healthcare.