Title: Leveraging Artificial Intelligence for Advanced 5-Year Early Detection and Monitoring of Fahr Syndrome: A State of the Art
Authors: Abdullah Al Aamri , Mourad M.H Henchiri
Volume: 9
Issue: 7
Pages: 29-36
Publication Date: 2025/07/28
Abstract:
Fahr Syndrome is a rare neurodegenerative disorder characterized by progressive brain calcifications, typically diagnosed only after symptom onset, which limits timely intervention. This study proposes a novel approach leveraging deep reinforcement learning (DRL) to enable advanced early detection and continuous monitoring of Fahr Syndrome up to five years before clinical manifestation. By analyzing longitudinal CT scan images, the DRL model learns to identify subtle, preclinical changes in brain calcifications that are often imperceptible to conventional methods. Integrating multimodal data including imaging, genetic, and biochemical markers, the framework aims to provide a personalized, adaptive prediction system that enhances diagnostic accuracy and facilitates proactive management. This research highlights the potential of AI-driven techniques to transform Fahr Syndrome diagnosis, offering improved patient outcomes through earlier intervention.