Researchers at the University of California, San Francisco (UCSF) have developed an artificial intelligence model capable of predicting cognitive impairment and Alzheimer's disease progression using only a single baseline MRI scan and basic demographic information.
The approach, published in Nature Aging, could help make early Alzheimer's assessment faster, more accessible, and less dependent on costly specialized testing.
The researchers instead focused on extracting clinically meaningful information from a single baseline MRI scan.
The framework was trained to perform several related tasks simultaneously, including tissue segmentation, Alzheimer's diagnosis prediction, and estimation of both present and future cognitive performance.
A key innovation of the study was the development of a specialized image model that segments brain tissue into gray matter, white matter, and cerebrospinal fluid before generating cognitive predictions.
According to the authors, this task-specific segmentation step allowed the model to learn biologically relevant spatial brain features more effectively than standard transfer-learning approaches.
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