Cambridge Researchers Develop New AI Tool for Early Detection of Alzheimer’s Disease 

Impacting over 55 million people worldwide, dementia presents a significant global healthcare challenge. It costs $820 billion every year, and the number of people affected by dementia is anticipated to triple over the next 50 years. 

Alzheimer's disease is the most common cause of dementia, accounting for 60–80% of cases. The complexity of Alzheimer's disease along with its slow and subtle progress makes early diagnosis difficult, resulting in delayed treatment and missed opportunities for patients and families to plan for necessary support.

Researchers from the University of Cambridge may have uncovered a breakthrough that can revolutionize Alzheimer's diagnosis. The researchers have developed a new tool that outperforms clinical tests in predicting the progress of Alzheimer’s disease. 

The AI tool can predict whether people with early signs of Alzheimer’s disease will remain stable or develop the condition in four out of five cases. This makes the tool three times more accurate than standard clinical markers. 

 “We’ve created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer’s – and if so, whether this progress will be fast or slow,” said Professor Zoe Kourtz, Senior author of the research paper and Professor at the Department of Psychology at the University of Cambridge. 

Koutz also expressed that he believes the new tool has the potential to improve patient well-being by identifying individuals who need the most intensive care. It will also help reduce anxiety for patients predicted to remain stable. 

The study was funded by the National Institute for Health Research Cambridge Biomedical Research Centre, the Alzheimer’s Drug Discovery Foundation Diagnostics Accelerator, Wellcome, the Royal Society, the Alan Turing Institute, and Alzheimer’s Research UK. The Cambridge researchers also collaborated with a cross-disciplinary team from the University of Birmingham and the National University of Singapore.

Early diagnosis of Alzheimer’s is crucial as that is when treatment is most effective. However, early detection through traditional methods may not be accurate without the use of expensive and invasive tests such as positron emission tomography (PET) scans or lumbar puncture, which are not commonly available at most memory clinics. 

The AI model was developed using low-cost and non-invasive patient data, including structural MRI scans and cognitive tests, to analyze gray matter atrophy of over 400 individuals. 

The gray matter in the brain is composed of neuronal cell bodies crucial for performing various cognitive functions. Reduced density or volume of gray matter is often associated with neurodegenerative conditions such as Alzheimer's disease. 

The researchers trained and built a predictive prognostic model (PPM) that analyzes gray matter atrophy and other clinically relevant predictors such as cognitive tests. The predictions were compared and validated with independent real-world data from different memory clinics across countries. The findings revealed that the tool was successful in identifying individuals who went on to develop Alzheimer’s in 82% of the cases and correctly identifying those who didn't in 81% of cases. 

The model categorized Alchemier’s patients into three groups: those whose symptoms would remain stable (around 50% of participants), those who would progress to Alzheimer's slowly (around 35%), and those who would progress more rapidly (the remaining 15%).

The AI algorithm’s robustness was confirmed by further testing the model using patient data from 600 participants from the US and longitudinal data collected from 900 individuals from memory clinics in the UK and Singapore. 

The research team is confident that their AI model is applicable in real-world patient and clinic settings. To ensure the AI model is generalizable to a real-world setting, the researchers used routinely collected data from actual memory clinics and research cohorts. 

Looking ahead, the research team plans to extend the model’s application to other forms of dementia, such as vascular dementia and frontotemporal dementia, by incorporating other types of data such as blood test markers.  

AI has unlocked new possibilities in disease research, revealing insights that were previously inaccessible. It has been instrumental in enabling researchers to identify Alzheimer’s drug targets. Insilico Medicine and the University of Cambridge jointly published a paper outlining a novel AI-based technique to pinpoint specific proteins that a drug can interact with to treat the disease. These advancements highlight AI's potential to transform healthcare and lead to more reliable diagnoses and treatments for Alzheimer's.

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