Dates: 2022
Principal Investigator: Dr. Elsayed Elalfy
Funded by: SDAIA-KFUPM Joint Research Center for Artificial Intelligence

Description: Antimicrobial drugs are essential for treating infections, but antimicrobial resistance (AMR) poses a growing threat, making treatments less effective and harder to manage. Personalized medicine is needed, yet challenges remain due to patient variability and privacy concerns in genomic data. This study proposes developing a decentralized diagnostic tool using deep and federated learning to identify key DNA segments linked to AMR, enabling faster and cheaper point-of-care tests. It also aims to build a predictive model to guide effective antimicrobial prescriptions while protecting data privacy—helping personalize treatments, reduce AMR, and extend the usefulness of existing drugs.