As people age, the brain undergoes multiple changes, increasing the vulnerability to neurological diseases. Early detection of neurological diseases is critical for effective treatment. In a recent study, researchers from China demonstrated that machine learning algorithms integrating imaging, genetic, and clinical data can significantly improve diagnostic accuracy and prediction, enabling timely interventions and better patient outcomes. This approach may transform clinical practice by improving precision, speed, and interpretability in assessing complex brain disorders.

As Alzheimer’s disease (AD) becomes more prevalent, scientists are exploring new methods for its early detection. In a recent review, researchers from India outline how neuron-derived extracellular vesicles—tiny membrane-bound particles released by neurons—are emerging as a promising diagnostic tool for AD. The contents of these vesicles carry molecular clues on the health of neurons, and it may soon be possible to use them to predict the onset of AD several years in advance.