Tracing Alzheimer's Genetic Footprints: A Pioneering Longitudinal Study Using Artificial Intelligence to Unravel Mutation-Driven Risks and Progression in Virtual Patients; Part 2 – The APP, PSEN1 and PSEN2 mutations

Abstract Alzheimer’s Disease (AD), the most prevalent neurodegenerative disorder, presents complex challenges in early diagnosis and management. This study, leveraging the innovative aiHumanoid platform, explores the genetic underpinnings of AD through a longitudinal analysis of mutations in APP, PSEN1, and PSEN2 genes. Using virtual patient simulations that integrate extensive medical and genetic literature, we examined the influence of these mutations on disease progression and biomarker development from early childhood through advanced age. Our findings reveal distinct progression patterns associated with these genetic variants, highlighting their potential in identifying early biomarkers, and refining diagnostic strategies. This part of our research broadens our understanding by including a wider array of genetic factors, thereby enhancing the potential for personalized treatment approaches. Ethical considerations regarding the use of AI in research were carefully addressed to ensure responsible technology integration into healthcare settings. The insights gained emphasize the transformative potential of AI in advancing AD research, laying a groundwork for future innovations in diagnostic and therapeutic methodologies.