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AI applications in neurological diagnostics

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2026-08-22 12:01 UTC → 2026-08-25 07:11 UTC · added ~~removed~~

Researchers are increasingly applying artificial intelligence to improve the diagnosis and monitoring of neurological conditions. In the United States, scientists have developed deep-learning systems to map brain aging and dementia risk. At the University of Southern California, a system led by Associate Professor Andrei Irimia uses MRI scans to identify specific brain regions that age at different ~~rates, rather than providing a single age for the entire organ.~~ rates. Additionally, researchers from the University of California, San Francisco, and Beth Israel Deaconess Medical Center have developed a machine-learning model that analyzes EEG recordings during sleep. By examining microscopic characteristics of brain waves, ~~such as delta waves and sleep spindles,~~ the system calculates a ‘brain age’. Findings indicate that a 10-year gap between estimated brain age and chronological age correlates with a nearly 40 percent increase in dementia risk. Further advancements include the development of a system using a model called SigLIP, which combines brain MRI images with clinical data such as age, sex, and memory test results. In a study of 416 individuals, this method achieved an AUC of 0.91 for predicting the risk of progression to Alzheimer’s disease over a four-year period, outperforming models based solely on memory scores (0.85) or specific ~~biomarkers.~~ biomarkers (0.73). Researchers ~~note~~ emphasize that while promising, the AI is not a replacement for medical professionals. In the Czech Republic, studies from the University of Ostrava and associated institutions have demonstrated that AI can serve as a reliable tool for monitoring multiple sclerosis activity. Automated assessments have matched expert radiologist evaluations in 91.8 percent of ~~cases, assisting in tracking disease progression~~ cases. Neurologist Ondřej Volný noted that the technology acts as a tool to increase precision, particularly during follow-up examinations by identifying subtle changes, such as new or enlarging lesions, through MRI-based ~~lesion~~ segmentation and brain volume analysis.

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