AI detects dementia types brain scan

AI Tool Detects 9 Dementia Types from a Single Brain Scan: Mayo Clinic Breakthrough

Breakthrough AI Tool from Mayo Clinic Enables Early and Accurate Dignosis

Study Findings and Diagnostic Performance

Researchers at the Mayo Clinic have unveiled an AI-driven tool enabling clinicians to detect brain activity signatures associated with nine forms of dementia—including Alzheimer's—via a single, commonly used scan, marking a significant leap forward in early and precise diagnosis.

According to a study published on 27 June 2025 in Neurology. StateViewer correctly identified dementia types in 88% of cases. The AI tool also expedited scan interpretation, achieving nearly double the speed and up to triple the accuracy of traditional diagnostic approaches. The system was trained and evaluated using more than 3,600 brain images, including those from healthy participants.

Addressing Diagnostic Challenges in Dementia Care

This breakthrough tackles a fundamental hurdle in dementia care—achieving early and accurate diagnosis, even in cases involving overlapping conditions. With new therapies on the horizon, prompt identification ensures patients receive the most suitable treatment when it proves most effective. The tool offers advanced diagnostic capabilities to practices without  specialist neurology support.

The Growing Burden of Dementia

Global Impact and Current Diagnostic Limitations

Dementia currently impacts over 55 million individuals worldwide, with almost 10 million new diagnoses annually. Alzheimer's disease, the predominant subtype, now ranks as the fifth most common cause of death. Diagnosis involves memory assessments, blood tests, scans, clinical evaluations and specialist referrals—yet even experts find it difficult to differentiate between Alzheimer's, Lewy body and frontotemporal dementias.

StateViewer: A Vision Realized through AI Collaboration

Leadership Behind the Innovation

StateViewer was created under the leadership of Dr. David Jones a consultant neurologist and head fo the Neurology Artificial Intelligence Programme at the Mayo Clinic.

"Each patient who enters my clinic brings with them a story uniquely influenced by the brain's intricate workings," says Dr. Jones. "That complexity is what attracted me to neurology and still fuels my dedication to clearer diagnoses. StateViewer embodies that passion—progress toward earlier insight, more accurate treatment and ultimately, altering the course of these conditions."

AI Engineering and Patient-Focused Design

To realize that vision, Dr. Jones collaborated with Leland Barnard, Ph.D. the data scientist spearheading the AI engineering of StateViewer.

"Throughout the development of StateViewer, we remained acutely aware that each data point and brain scan represented a person grappling with a challenging diagnosis and pressing concerns," says Dr. Barnard. "Witnessing how this tool can offer clinicians timely, accurate insights underscores the promise of machine learning within clinical practice."

Translating Brain Activity into Clinical Understanding

How the StateViewer Tool Works

The tool interprets an FDG-PET scan—used to assess how the brain metabolizes glucose energy—and cross-references it with a comprehensive database of scan from individuals with confirmed dementia, detecting patterns linked to distinct or overlapping dementia types.

Dementia Subtype Detection

Alzheimer's generally impacts regins associated with memory and cognition, while Lewy body dementia affects area governing attention and motor function. Frontotemporal dementia, meanwhile disrupts language and behavioural centers.

Visual Insights for All Clinicians

StateViewer employs colour-coded brain maps to visually depict these patterns, enabling all clinicians—even those without neurology backgrounds—to comprehend the AI's diagnostic rationale.

Future Prospects and Clinical Integration

Researchers at the Mayo clinic intend to broaden the tool's application and will carry on assessing it effectiveness across diverse clinical environemtns.

Source

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