IBM’s machine learning model can detect Alzheimer’s, slow down its progression

By Digit NewsDesk | Published on 11 Mar 2019
IBM’s machine learning model can detect Alzheimer’s, slow down its progression

IBM claims that its Machine Learning models could one day help clinicians to predict risk of Alzheimer’s with an accuracy of up to 77 percent.


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  • IBM says its machine learning model can help detect Alzheimer’s disease in early stages.
  • This model could help clinicians to find ways to slow down the progression of the disease.
  • The model is also said to help researchers find accurate samples to get better results.  

Alzheimer’s disease is a terminal neurodegenerative disease that has been historically diagnosed based on observing significant memory loss in patients, but this could change soon. IBM has claimed that it has developed a machine learning model that can help clinicians predict the severity of the Alzheimer’s disease as well as slow its progression. The company’s Australian arm, IBM Research – Australia, undertook a research and published its findings in journal Scientific Reports.

The research could be a breakthrough amid failed clinical trials that have been conducted since 2002 to find a cure or a modifying therapy for this illness. It is thought that the high failure rate of these trials may be because the people enrolled are in the latest stages of the disease, and have likely already suffered a level of brain tissue loss that cannot easily be repaired. IBM says that its machine learning can also help researchers find samples with mild cognitive impairment and get better results.

Citing a previous research, IBM said that the biological marker associated with the disease, a peptide called amyloid-beta, changes long before any memory-related issues are apparent. Examining the concentration of the peptide in an individual’s spinal fluid provides an indication of risk decades before any memory related issues occur. The company says that accessing spinal fluid is highly invasive and is expensive to conduct on large segments of the population.

“Hence, there is a strong effort in the research community to develop a less invasive test, such as a blood test, that can yield information about Alzheimer’s disease risk,” IBM said. The paper published by the team shows how the team used its machine learning model to identify a set of proteins in blood that can predict the concentration of amyloid-beta in spinal fluid.

“The models we built could one day help clinicians to predict this risk with an accuracy of up to 77 percent. While the test is still in the early phases of research, it could potentially help improve the selection of individuals for drug trials: individuals with mild cognitive impairment who were predicted to have an abnormal concentration of amyloid in their spinal fluid were found to be 2.5 times more likely to develop Alzheimer’s disease,” Ben Goudey, Staff Researcher, Genomics Research Team, IBM Research, wrote in a blog post.

IBM also claims that amid a wide range of other proposed blood tests for Alzheimer’s disease are being developed, this is the first study to use a machine learning approach to identify sets of proteins in blood that are predictive of a biomarker in spinal fluid. The findings of the research will be presented at the 14th​ International Conference on Alzheimer’s and Parkinson’s Diseases, which is scheduled to take place from March 26-31 in Lisbon, Portugal.

“At IBM Research, our mission is to use AI and technology to understand how to help clinicians better detect and ultimately prevent these diseases in their early stages. Whether that’s through retinal imaging, blood biomarkers or minor changes in speech, we envision a future in which health professionals have a wide array of easily accessible data available to more clearly identify and track the onset and acceleration of these conditions,” Goudey added.

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