New Delhi, July 31 Mayo Clinic scientists are leveraging artificial intelligence (AI) and machine learning to analyse electroencephalogram (EEG) tests with increased speed and precision. This advancement aims to help neurologists identify early signs of dementia that are often missed in traditional analyses.
The EEG, a century-old test involving electrodes placed on the scalp to monitor brain activity, is commonly used to detect epilepsy. However, recent research published in Brain Communications by the Mayo Clinic Neurology AI Program (NAIP) reveals that AI can enhance the detection of subtle abnormal patterns in EEG data, which may indicate cognitive issues such as Alzheimer's disease and Lewy body dementia.
"There's a wealth of medical information in these brain waves," said Dr. David T. Jones, senior author and director of NAIP. "We found that AI could accurately measure and quantify changes in EEG patterns associated with cognitive problems."
The study involved data from over 11,000 patients who underwent EEGs at Mayo Clinic over a decade. Researchers used machine learning to distill complex brain wave patterns into six specific features, enabling the AI to focus on indicators of cognitive issues.
"It was remarkable how the technology quickly extracted EEG patterns compared to traditional methods like cognitive tests and brain imaging," noted Dr. Wentao Li, co-first author of the study.
While EEGs may not replace other diagnostic tools like MRIs or PET scans, they offer a more accessible and economical option, especially in areas lacking advanced medical facilities. Dr. Jones emphasized the importance of early diagnosis for better treatment outcomes.
"This method could provide a cheaper way to identify early memory loss or dementia," Dr. Jones said. The team plans further research to integrate these tools into clinical practice and expand their use beyond Mayo Clinic.
The study highlights the potential for AI-enhanced EEGs to revolutionise dementia diagnosis and treatment, offering new hope for early intervention in cognitive disorders.
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