In an attempt to develop effective diagnostics tests for Alzheimer’s, IBM has introduced machine learning which the company believes that someday it will help in early detection and creation of stable and effective diagnostic tests for early-onset Alzheimer’s, according to research conducted by IBM’s Australian team.
The research document says:
“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.”
In its medical report on Monday, IBM said that Artificial Intelligence can be used to replace invasive and expensive tests for the disease which can be of great help for Alzheimer’s patients.
As of now, there is no cure for Alzheimer’s disease despite the fact that a number of tests have been conducted since 2002. While a number of blood tests are being developed, it is the first time, machine learning is being used to identify sets of proteins in the blood that are predictive of a biomarker in spinal fluid.
Early diagnosis of the disease can help in efficient management of the disease and can be of great help for the patients and their families. The publication of the result has come just at the right time when diseases such as Parkinson’s, Alzheimer’s and Huntington’s are affecting millions of people around the world. The main aim of the project is to help clinicians better detect and ultimately prevent these diseases in their early stages.
The research team at IBM believes that the use of machine learning can predict risk factors in the future with an accuracy of up to 77 percent which can play an important role in developing the cure of this disease.