AI model can detect multiple cognitive brain diseases from a single blood sample

Reddit r/artificial / 4/2/2026

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Researchers at Lund University report an AI model that can identify multiple neurodegenerative brain diseases from a single blood sample, addressing overlapping symptom profiles that complicate diagnosis.
The work, published in Nature Medicine, was trained on protein measurements from more than 17,000 patients and controls drawn from multiple datasets in the GNPC proteomics database.
The approach uses advanced statistical learning with “joint learning” to learn a shared protein pattern linked to neurodegeneration and then apply it to distinguish specific diseases.
The study says the model outperforms earlier AI-based diagnostics and aims to enable accurate multi-disease diagnosis via a single blood test in the future.

AI model can detect multiple cognitive brain diseases from a single blood sample

The symptom profiles of different neurodegenerative diseases often overlap, and diagnosing age-related cognitive symptoms is complex. A patient may have multiple overlapping disease processes in the brain at the same time, for example, Alzheimer's disease and Lewy body disease, especially in the early stages of cognitive decline. Now, researchers at Lund University have developed an AI model showing that it is possible to detect several neurodegenerative diseases from a single blood sample. Their paper is published in the journal Nature Medicine.

Researchers Jacob Vogel and Lijun An, together with colleagues from the Swedish BioFINDER study and the Global Neurodegenerative Proteomics Consortium (GNPC, an international research consortium that has created the world's largest proteomics database for neurodegenerative diseases) have developed the AI model based on protein measurements from more than 17,000 patients and control participants, collected from several datasets within GNPC's proteomics database, the largest in the world for proteins related to neurodegenerative diseases.

"Our hope is to be able to accurately diagnose several diseases at once with a single blood test in the future," says Vogel, who led the study. He is an assistant professor, head of a research group, and part of the strategic research area MultiPark at Lund University.

Using advanced statistical learning methods and a process known as "joint learning," the researchers' AI model was able to identify a specific set of proteins that form a general pattern for diseases involving brain degeneration. This learned pattern was then used to diagnose different neurodegenerative diseases. Vogel confirms that their AI model outperforms previous models, while also being able to diagnose five different dementia-related conditions: Alzheimer's disease, Parkinson's disease, ALS, frontotemporal dementia, and previous stroke.

The study stands out compared to similar research because the model's results were validated across multiple independent datasets, according to the researchers.

"We also found that the protein profile predicted cognitive decline better than the clinical diagnosis did, and it seems like individuals with the same clinical diagnosis may have different underlying biological subtypes," says An, the study's first author.

Many individuals diagnosed with Alzheimer's disease showed a protein pattern more similar to other brain disorders. "This could mean they have more than one underlying disease, that Alzheimer's can develop in multiple ways, or that the clinical diagnosis is incorrect. However, I don't think current protein measurements from blood samples will be sufficient on their own to diagnose multiple diseases. We need to refine the method and combine it with other clinical diagnostic tools," says Vogel.