HomeTech NewsArtificial Intelligence Detects Parkinson's Before Symptoms Appear

Artificial Intelligence Detects Parkinson’s Before Symptoms Appear

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A new breakthrough in early detection of Parkinson’s uses artificial intelligence to identify underlying signs of the disease years before symptoms appear. The tool, called CRANK-MS, is based on an analysis of blood metabolites and has shown an accuracy of up to 96%. This promising discovery could allow for earlier diagnosis and more effective treatment for patients.

A new hope in the fight against Parkinson’s

Parkinson’s is a neurodegenerative disease that affects millions of people around the world. Until now, the diagnosis of this disease has been challenging, as symptoms often appear after significant damage to the brain has already occurred. However, scientists are working hard to find ways to detect it early, when treatments may be most effective.

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In this context, a team of researchers has developed a revolutionary tool based on artificial intelligence that can identify signs of Parkinson’s before motor symptoms appear. The tool, called CRANK-MS, uses an analysis of metabolites in the blood to detect patterns that could predict the presence of the disease or protect against it.

The magic of metabolites and machine learning

Metabolites are chemical compounds produced by the body when it breaks down food, medicines, or chemicals. The researchers analyzed blood plasma samples collected in a previous study and focused on 39 patients who developed Parkinson’s within 15 years, compared with 39 control patients who did not develop the disease. During the analysis, several potentially significant metabolite patterns were identified.

The research team used layers of neural network nodes, modeled on the human brain, to examine associations between metabolites. Unlike conventional approaches that focus on correlations between specific molecules, CRANK-MS takes into account associations between multiple metabolites, which enhances its predictive ability.

Promising discoveries and amazing precision

The study revealed that CRANK-MS was able to detect the risk of developing Parkinson’s with up to 96% accuracy. This is partly due to the large amount of data collected and processed by the system without the need to manually simplify or filter the information. The researchers highlighted the importance of this approach, as it makes it possible to identify metabolites that might have been missed with traditional methods.

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One of the interesting findings was decreased levels of triterpenoids, compounds found in foods such as apples, olives and tomatoes, in people who developed Parkinson’s. In addition, the presence of polyfluorinated alkyl substances (PFAS) was observed in those who subsequently developed the disease. While it is suspected that this may be related to increased exposure to industrial chemicals, larger studies are needed to confirm this connection.

The way forward and hope for the future

Although this study was based on a relatively small sample, the results are promising and open up new perspectives in the early detection of Parkinson’s. The researchers are working on testing CRANK-MS in larger cohorts and in different regions of the world to confirm and validate its effectiveness. The availability of this tool to other scientists also opens the possibility of detecting other diseases through blood samples.

Early detection of Parkinson’s can have a significant impact on the lives of patients, allowing early therapeutic interventions and delaying the progression of symptoms. Furthermore, the innovative approach of combining artificial intelligence and metabolite analysis opens new doors in medical research and shows the potential of the technology to address complex health challenges.

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