There is strong evidence that machine learning can be very helpful in diagnosing Alzheimer’s disease. The latest study, conducted by researchers at the University of California, San Diego School of Medicine (UCSD), uses artificial intelligence to detect amyloid plaques on the brains of deceased patients, automating the work normally done by pathologists. These results conclude that “machine learning is able to analyze the type of amyloid plaque in the brain. 

One of the difficulties with Alzheimer’s disease is that until we can make a definitive diagnosis, too many neurons have died. Beta-amyloid plaques are the most common type of amyloid plaque in the brain, which is responsible for the destruction of nerve connections. 

According to the researchers, the accumulation of beta-amyloid plaques in the brain leads to Alzheimer’s, making the disease essentially irreversible.

The devastating neurodegenerative disease Alzheimer’s is incurable and patients need treatment to slow the progression of the disease before major symptoms appear. Scientists are working tirelessly to develop new techniques to identify the symptoms of Alzheimer’s, potentially enabling doctors to intervene in treatment, largely thanks to artificial intelligence. Detecting and using artificial intelligence to detect dementia is very expensive, but patients should seek treatment as soon as possible, even if they slow the progression of the disease before major symptoms appear, the researchers said.

Applying AI to MRI scans

By applying artificial intelligence algorithms to MRI brain scans, researchers are developing methods to automatically distinguish between two early forms of dementia that can be precursors to memory – and eradicate the disease. In the summer of 2016, for example, a team of researchers based in the Netherlands successfully trained an algorithm to distinguish the symptoms of Alzheimer’s disease in patients with mild cognitive impairment (MDA) from those without the disease on MRI scans. This suggests that the approach could allow automated screening in centers without experienced neuroradiologists. 

A new study published in the journal Radiology shows that artificial intelligence (AI) can improve brain imaging by predicting the onset of Alzheimer’s disease. The approach trains an algorithm to analyze PET scans of the brain to detect functional irregularities that often occur when brain architecture deteriorates. In 2017, a team of McGill researchers trained artificial intelligence to predict the early stages of Alzheimer’s using the amyloid protein discovered in the PET scan. 

The study found that using artificial intelligence can help doctors recognize Alzheimer’s before making a medical diagnosis, opening the door to more effective treatments, and more time for financial, legal, and personal preparation. Early diagnosis means a better diagnosis, better treatment options for patients, and better care for their families. 

While there is no permanent cure for Alzheimer’s, promising drugs are emerging that can help stem the disease’s progression. With increasing research every day, new ways of diagnosing Alzheimer’s, dementia in all its forms, are being tested. By using common forms of brain scans, researchers are able to program machine-learning algorithms that can diagnose the early stages of Alzheimer’s, giving doctors a possible chance to begin treatment. However, due to the high risk of side effects and long-term consequences, treatment must be administered early in the course of the disease in order to do its best. 

The race against the clock has inspired scientists to look for ways to diagnose the disease earlier. One of the difficulties of Alzheimer’s disease is that too many neurons have already died by the time clinical symptoms appear, making the disease essentially irreversible. However, a definitive diagnosis can only be made after the onset of symptoms and only after several years of treatment. 

Prediction of Alzheimer’s

In a recent study published in Radiology, Sohn combined neuroimaging with machine learning to predict which patients would develop Alzheimer’s when they first developed memory problems. PET scans, which measure levels of certain molecules such as glucose in the brain, are studied as a tool for diagnosing Alzheimer’s disease before symptoms become severe and intervene at the best of times. If the algorithm stands up to the test, he believes it could be used by neurologists who see patients without memory in a clinic as a predictive diagnostic tool for Alzheimer’s disease, helping them get the treatment they need earlier.

The deep learning algorithm developed for the early detection of Alzheimer’s disease achieved a specificity of 82% with fluoro18 fluorodeoxyglucose (PET) in the brain, compared to a radiological reader. It was developed and validated by researchers at the University of California, San Diego School of Medicine and the National Institute of Neurological Disorders and Stroke. 

They collected data from neuroimaging studies, imaging studies, and retrospective independent testing of patients with Alzheimer’s disease and the National Institute of Neurological Disorders and Stroke (NIH). 

The process produced astonishing results and proved that deep-learning algorithms can accurately predict Alzheimer’s disease in the earliest stages of its development. Further studies are being developed to confirm the accuracy of the algorithm’s predictions for Alzheimer’s and other neurodegenerative diseases. Although early Alzheimer’s is a race against time, this algorithm could give doctors a better chance of treating the disease before irreversible and widespread brain atrophy causes significant brain volume loss.