Transforming neurodegenerative disorder care with machine learning: Strategies and applications

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting in diverse forms such as memory loss and movement disorders, pose significant challenges due to their complex molecular mechanisms and heterogeneous patient presentations. Diagnosis often relies heavily on clinical assessments and neuroimaging, with definitive confirmation frequently requiring post-mortem autopsy. However, the emergence of Artificial Intelligence (AI) and Machine Learning (ML) offers a transformative potential. These technologies can enable the development of non-invasive tools for early diagnosis, biomarker identification, personalized treatment strategies, patient subtyping and stratification, and disease risk prediction. This review aims to provide a starting point for researchers, both with and without clinical backgrounds, who are interested in applying ML to NDs. We will discuss available data resources for key diseases like Alzheimer's and Parkinson's, explore how ML can revolutionize neurodegenerative care, and emphasize the importance of integrating multiple high-dimensional data sources to gain deeper insights and inform effective therapeutic strategies.