IBM hopes machine learning is the key to solving Alzheimer’s

IBM has developed a machine learning algorithm which shows promise for detecting and slowing the progress of Alzheimer’s disease.

Alzheimer’s is a brutal disease not just for sufferers, but their loved ones too. The disease currently has no cure and causes an increasing loss of memory, confusion, and difficulty completing once familiar tasks.

IBM Australia published a paper today providing details of how machine learning and AI can be used to predict the severity of the disease and help to slow its progression.

Ben Goudey, Staff Researcher of the Genomics Research Team at IBM Research, wrote:

“Neurodegenerative diseases such as Parkinson’s, Alzheimer’s and Huntington’s are affecting millions of people around the world. While these mysterious and crippling diseases do not yet have a cure, the answer to slowing their growth may lie in prevention.

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.”

Early diagnosis helps to prepare the sufferer and their loved ones as much as possible before degeneration takes hold. Official diagnosis also helps to make the patient available for medical trials with the hope of one day finding a full cure.

Hundreds of Alzheimer’s medical trials have been conducted since the early 2000s but with a high failure rate. Some believe this failure rate is due to late detection of the disease when there’s already significant brain tissue loss.

Research suggests a peptide called amyloid-beta changes long before memory loss occurs. Analysing the concentration of this peptide from an individual’s spinal fluid could highlight the risk decades in advance.

Accessing spinal fluid is an invasive and expensive procedure. In his post, Goudey wrote: “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.”

Using their model, IBM predicts they could help clinicians to predict the risk of Alzheimer’s with an accuracy of up to 77 percent. Goudey said his team’s approach can be extended to other spinal fluid-based biomarkers.

The full paper is published in the science journal Nature here.

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