Mayo Clinic researchers have developed a brand new synthetic intelligence (AI) instrument that helps clinicians establish mind exercise patterns linked to 9 kinds of dementia, together with Alzheimer’s illness, utilizing a single, extensively accessible scan – a transformative advance in early, correct prognosis.
The instrument, StateViewer, helped researchers establish the dementia sort in 88% of circumstances, based on analysis printed on-line on June 27, 2025, in Neurology, the medical journal of the American Academy of Neurology. It additionally enabled clinicians to interpret mind scans almost twice as quick and with as much as thrice higher accuracy than commonplace workflows. Researchers educated and examined the AI on greater than 3,600 scans, together with photos from sufferers with dementia and folks with out cognitive impairment.
This innovation addresses a core problem in dementia care: figuring out the illness early and exactly, even when a number of circumstances are current. As new remedies emerge, well timed prognosis helps match sufferers with probably the most applicable care when it could have the best impression. The instrument might convey superior diagnostic help to clinics that lack neurology experience.
The rising toll of dementia
Dementia impacts greater than 55 million individuals worldwide, with almost 10 million new circumstances annually. Alzheimer’s illness, the most typical type, is now the fifth-leading explanation for dying globally. Diagnosing dementia usually requires cognitive checks, blood attracts, imaging, medical interviews and specialist referrals. Even with intensive testing, distinguishing circumstances corresponding to Alzheimer’s, Lewy physique dementia and frontotemporal dementia stays difficult, together with for extremely skilled specialists.
StateViewer was developed below the course of David Jones, M.D., a Mayo Clinic neurologist and director of the Mayo Clinic Neurology Synthetic Intelligence Program.
Each affected person who walks into my clinic carries a singular story formed by the mind’s complexity. That complexity drew me to neurology and continues to drive my dedication to clearer solutions. StateViewer displays that dedication – a step towards earlier understanding, extra exact remedy and, in the future, altering the course of those ailments.”
David Jones, M.D., Mayo Clinic neurologist
To convey that imaginative and prescient to life, Dr. Jones labored alongside Leland Barnard, Ph.D., a knowledge scientist who leads the AI engineering behind StateViewer.
“As we have been designing StateViewer, we by no means overlooked the truth that behind each knowledge level and mind scan was an individual dealing with a tough prognosis and pressing questions,” Dr. Barnard says. “Seeing how this instrument might help physicians with real-time, exact insights and steerage highlights the potential of machine studying for medical drugs.”
Turning mind patterns into medical perception
The instrument analyzes a fluorodeoxyglucose positron emission tomography (FDG-PET) scan, which reveals how the mind makes use of glucose for vitality. It then compares the scan to a big database of scans from individuals with confirmed dementia diagnoses and identifies patterns that match particular varieties, or combos, of dementia.
Alzheimer’s usually impacts reminiscence and processing areas, Lewy physique dementia includes areas tied to consideration and motion, and frontotemporal dementia alters areas liable for language and conduct. StateViewer shows these patterns by way of color-coded mind maps that spotlight key areas of mind exercise, giving all clinicians, even these with out neurology coaching, a visible rationalization of what the AI sees and the way it helps the prognosis.
Mayo Clinic researchers plan to develop the instrument’s use and can proceed evaluating its efficiency in a wide range of medical settings.
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Journal reference:
Barnard, L., et al. (2025). An FDG-PET–Based mostly Machine Studying Framework to Help Neurologic Resolution-Making in Alzheimer Illness and Associated Problems. Neurology. doi.org/10.1212/wnl.0000000000213831.
