| Abstrakt: |
A recent study conducted by researchers at I.M. Sechenov First Moscow State Medical University in Russia focused on developing precise models for the progression of Alzheimer's disease (AD) using deep neural networks. The study utilized volumetric measurements obtained through magnetic resonance imaging (MRI), trajectories of cognitive assessments, and clinical status indicators from 150 patients diagnosed with AD. The researchers found that a multi-task learning approach enhanced the accuracy of predicting the progression of AD by 14.8%. However, it is important to note that the study's generalizability may be limited due to the restricted dataset and specific population under examination. These findings contribute to the advancement of more precise diagnosis and treatment of AD. [Extracted from the article] |
