Note: I provide links to all of my sources. They are underlined and in boldface. I want my readers to be able to check out all sources for themselves.
Alzheimer’s disease is a horrible disease. It slowly destroys the afflicted person’s memory and thinking skills. Over time, the person has trouble carrying out even simple tasks. Alzheimer’s disease is the most common cause of dementia among older adults.
Dr. Alois Alzheimer was the one who discovered the disease in 1906. He noticed changes in the brain tissue of a woman who experienced memory loss, language problems, and unpredictable behavior. When she died, he found that her brain contained abnormal clumps and tangled fibers.
This horrible disease affects more than 6 million Americans. More than 11 million people help care for a loved one with Alzheimer’s. The grief of a loved one no longer recognizing you is tragic.
Recently, there came a cause for hope. A new algorithm has been found that allows detection of even early stage Alzheimer’s Disease. Up to now, this was a limitation of testing strategies. Obviously, the earlier you can detect signs of the disease, the better the chances to treat it and limit the progression.
The research was done in the United Kingdom.
Lead researcher Professor Eric Aboagye said that “currently no other simple and widely available methods can predict Alzheimer’s disease with this level of accuracy, so our research is an important step forward”.
The method uses a machine-learning algorithm. The methodology could predict the presence of Alzheimer’s with a 98% accuracy rate. It was even able to detect early stages with 79% accuracy, which is considered extremely high.
A standard 1.5 Tesla machine was used, which most hospitals already have.
The current methodology to diagnose Alzheimer’s is much more complex than a simple MRI. Many tests are done. They include not just an MRI but also CT scans, blood work, biomarker tests, and cognitive evaluations.
All of these tests have limited accuracy, which is why they are used in conjunction. Biomarker tests were shown in 2017 to have a 77% accuracy. MRIs were found in 2021 to falsely predict early Alzheimer’s about 50% of the time.
Aboagye stated that even patients who have multiple neurological conditions could be accurately diagnosed for Alzheimer’s with the new algorithm.
In order to create the model, an algorithm used for classifying cancerous tumors was modified. Researchers used 660 features such as size, shape, and texture as parameters. They also divided the brain into 115 discrete sections. The algorithm was trained to correctly identify predictors of the disease.
The algorithm was able to identify novel features that had not previously been associated with Alzheimer’s. These included areas of the cerebellum which are used to maintain balance and posture. Another area that was newly identified as predictive was an area that is linked to sensory and motor functions as well as sleeping cycles.
Thanks to these discoveries, new areas of research have been opened up.
The algorithm is used twice to come up with a diagnosis. This is because some people who have Alzheimer’s may also have other neurological conditions such as Parkinson’s disease or dementia. Additionally, the markers for Alzheimer’s will vary depending on the stage of the disease.
The two iterations of the algorithm are called Alzheimer’s Predictive Vector 1 (ApV1) and Alzheimer’s Predictive Vector 2 (ApV2).
ApV1 provides an initial diagnosis of Alzheimer’s. It examines 14 regions of the brain with 20 parameters. Cognitive scores and 19 other hallmarks across 12 regions are also used for this stage of testing.
Individuals who are diagnosed with Alzheimer’s are then given another scan with the ApV2 algorithm. This differentiates between early and late stages of the disease. While it also considers all prior hallmarks, it only looks at 8 regions.
Dr. Paresh Malhotra, a neurologist, observed that even specialists cannot detect some of the information that the algorithm is able to find. He stated that “using an algorithm able to select texture and subtle structural features in the brain that are affected by Alzheimer’s could really enhance the information we can gain from standard imaging techniques.”
I am fighting stage IV cancer. If you can help with medical bills, I would really appreciate it. Or if you enjoy my writing and would like to buy me a cup of coffee, that’s great too. Maybe someday I can return the favor.
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This post was previously published on Shefali O’Hara’s blog.
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