AI App Protects Brains From Dementia: New Tech & Millions at Risk

Researchers have developed an artificial intelligence tool capable of diagnosing nine types of dementia, including Alzheimer’s disease, with 88% accuracy using a single brain scan. The tool, named “State View” and developed by researchers at the Mayo Clinic, represents a significant advancement in the early detection of these debilitating conditions, according to a study published in the journal Neurology.

The AI analyzes patterns of brain activity, combining clinical data with routine scan results to achieve a diagnosis without the need for costly or complex testing. Its diagnostic speed is twice as fast as traditional methods, and delivers results three times more accurate, researchers say.

Simultaneously, a separate team of British researchers are leveraging artificial intelligence to analyze hundreds of thousands of brain scans in an effort to build a software tool for the early diagnosis of dementia. The collaborative project, called NEURii and launched a year ago, involves 20 experts from the Universities of Edinburgh and Dundee. The team will analyze CT scans and MRI data, alongside information from patient health records, to identify patterns indicative of potential dementia development.

The British study aims to create a digital tool for radiologists to use when examining patients, helping to identify those at risk of dementia and diagnose early stages of related diseases. Researchers believe isolating patients at risk will facilitate the development of more targeted treatments.

The NEURii project is expected to analyze 1.6 million images, in collaboration with the Public Benefit and Privacy Panel for Health and Social Care, part of NHS Scotland.

The advancements come as concerns grow about the increasing prevalence of dementia globally. While the Mayo Clinic’s “State View” offers a rapid and accurate diagnostic method, researchers hope the British project’s focus on early detection will lead to more effective interventions. The British team anticipates that early identification of at-risk patients will enable the development of more precise treatments for the disease.