Two of the researchers behind the AI model, Jacob Vogel and Lijun An, show the results of their study.
Photo Credit: Emma Nyberg.
Scientific Frontline: Extended "At a Glance" Summary: AI Model for Detecting Multiple Cognitive Brain Diseases
The Core Concept: A novel artificial intelligence model capable of identifying multiple neurodegenerative diseases simultaneously by analyzing complex protein patterns from a single blood sample.
Key Distinction/Mechanism: Unlike traditional diagnostics that test for individual diseases, this model utilizes a process called "joint learning" to identify overarching protein profiles associated with general brain degeneration. It accurately diagnoses and differentiates between five distinct dementia-related conditions—Alzheimer’s disease, Parkinson’s disease, ALS, frontotemporal dementia, and previous stroke—while predicting cognitive decline more effectively than standard clinical diagnoses.
Major Frameworks/Components:
- Joint Learning AI: Advanced statistical machine learning methods that process complex, interconnected data to find general biological patterns across multiple disease presentations.
- Proteomic Profiling: The systematic analysis of protein expression levels in biological samples to map biological functions and disease progression.
- GNPC Database Integration: The model was trained using protein measurements from over 17,000 patients and control participants, drawing from the world’s largest proteomics database for neurodegenerative diseases.
Branch of Science: Medicine, Neuroscience, Artificial Intelligence (Machine Learning), and Proteomics.
Future Application: The development of a standalone, reliable blood test capable of diagnosing overlapping cognitive disorders without the need for additional, invasive clinical instruments. It also opens avenues for discovering new disease-driving biological processes using mass spectrometry.
Why It Matters: Diagnosing age-related cognitive decline is inherently complex due to overlapping symptom profiles, such as those shared by Alzheimer’s disease and Lewy body disease. This AI-driven diagnostic tool can accurately detect cases where multiple disease processes occur simultaneously, significantly reducing misdiagnoses and accelerating the delivery of targeted treatments.
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. 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.
Different neurodegenerative conditions can present similar symptoms, making it difficult to distinguish between them, for example, Alzheimer’s disease and Lewy body disease, especially in the early stages of cognitive decline.
Our hope is to be able to accurately diagnose several diseases at once with a single blood test in the future.
Now, 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 an AI model capable of detecting multiple diseases at once. The model is 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 Jacob 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 Lijun 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 Jacob Vogel.
At the same time, he emphasizes that diagnostics is not the only application of their model. Many of the proteins that contributed to the AI model point to areas where follow-up studies could lead to a better understanding of the disease-driving processes behind these neurodegenerative conditions.
The next step is to include more proteomic markers using advanced methods such as mass spectrometry to identify patterns unique to each disease.
“We hope to inch closer toward a blood test that can make reliable diagnosis across disorders without aid from other clinical instruments,” says Jacob Vogel.
Funding: SciLifeLab and Wallenberg Data Driven Life Science Program, The Crafoord Foundation, The Swedish Research Council, US National Institutes of Health, The BioFINDER study was supported by the Alzheimer’s Association and others.
Published in journal: Nature Medicine
Title: A deep joint-learning proteomics model for diagnosis of six conditions associated with dementia
Authors: Lijun An, Alexa Pichet Binette, Ines Hristovska, Gabriele Vilkaite, Yu Xiao, Romina Zendehdel, Zijian Dong, Bart Smets, Rowan Saloner, Shinya Tasaki, Ying Xu, Varsha Krish, Farhad Imam, Shorena Janelidze, Danielle van Westen, The Global Neurodegenerative Proteomics Consortium (GNPC), Erik Stomrud, Christopher D. Whelan, Sebastian Palmqvist, Rik Ossenkoppele, Niklas Mattsson-Carlgren, Oskar Hansson, and Jacob W. Vogel
Source/Credit: Lund University | Martina Svensson
Reference Number: med033126_01
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