As we continue to learn about the progression of Alzheimer’s disease, it’s clear that early intervention is critical. Considering many experts believe that Alzheimer’s begins to develop 20+ years before symptoms become apparent, we need to ask ourselves \u2014 how can we properly diagnose this degenerative disease?<\/p>\n
What once flooded our TV screens, as Hollywood depicted our interaction with artificial intelligence (AI), is now a real-world intelligence with applications in modern medicine.<\/p>\n
Could AI detect changes in our brain\u201a leading to early, more effective treatment options?<\/p>\n
As researchers race to find a cure, some are focusing on blood samples, while others study cerebropsinal fluid. With all hands on deck, this research continues to be a global effort. This week, a group of researchers from the University of Bari, in Italy, are making headlines based on their unique algorithm.<\/p>\n
Based on this algorithm, they believe that it will become possible to spot tiny structural changes in the brain, a decade before symptoms develop. How did they do this? Here are some of main points of this recent study<\/a>:<\/p>\n Over the past couple of years, research has continued to develop alongside advances in technology. Machine learning has shown to be particularly useful, as computer programs learn to identify changes in the brain, relaying information based on the provided data.<\/p>\n Pairing unique MRI techniques with machine learning, allows researchers to measure tissue absorption rate, blood throughout the brain, and perfusion, in order to detect early warning signs of dementia.<\/p>\n Currently, with standard diagnostic MRI techniques, researchers are able to detect advanced changes in the brain. Although MRI scans can determine atrophy of the hippocampus, at this point, the brain tissue is already gone \u2014 there isn’t a way to restore that tissue. In that sense, it would be more helpful to diagnose Alzheimer’s well in advance.<\/p>\n Within one study<\/a>, published in Radiology<\/i>, researchers focused on arterial spin labeling (ASL) imaging. This technique is able to show how much blood is being delivered to various areas within the brain. Based on machine learning, the program can then recognize key patterns relating to cognitive impairment.<\/p>\n Using this system, the program was able to distinguish between patients with Alzheimer’s disease, subjective cognitive decline, and mild cognitive impairment. Using classifiers, researchers were then able to predict a diagnosis or possible progression with 82 to 90 percent accuracy.<\/p>\n The point here is, options such as ASL MRI screening can help identify changes in the brain, which appear during the earliest stages. It is during this time, that a window of opportunity may exist in terms of intervention. In turn, the rate of progression could be reduced, allowing for a more optimal prognosis.<\/p>\n In the absence of a cure, researchers continue to focus on risk reduction and early intervention. In order to so, a timely diagnosis needs to be made. As mentioned, by the time serious symptoms evolve, the patient’s brain tissue has already been damaged. Some of the benefits associated with early diagnosis include:<\/p>\n By 2050, it is believed that 135 million people will live with dementia worldwide. This is why it’s critical that we focus on preventative measures, reducing the global burden \u2014 but how?<\/p>\n\n
This Isn’t the First Time AI Has Been Associated with Dementia Research<\/h1>\n
The Earlier, the Better<\/h1>\n
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