Algorithm enables early diagnosis in a single MRI scan

  • Alzheimer’s disease is the most common form of dementia, affecting around 70% of people with dementia, but Alzheimer’s disease can be difficult to diagnose.
  • Doctors currently use several cognitive tests and scans to diagnose Alzheimer’s disease, which can take a long time.
  • Researchers have developed an algorithm to use with a single brain MRI to quickly detect early signs of Alzheimer’s disease.
  • In their trial, the system detected 98% of Alzheimer’s disease cases.

Dementia is, according to the World Health Organization, the seventh root cause deaths worldwide. The most common form, affecting up to 70% of those with a diagnosis of dementia, is Alzheimer’s disease.

People suspected of having Alzheimer’s disease usually undergo several tests to diagnose the disease. During the assessment, the person:

  • Give their medical history, both physical and mental.
  • Pass a medical examination.
  • Undergo a neurological exam to test reflexes, speech and coordination.
  • Take several cognitive tests to assess memory, thinking, and simple problem solving.
  • Have a magnetic resonance imaging (MRI) or CT scan to look for any changes in the brain, such as atrophy or shrinkage of the hippocampus.
  • Undergo cerebrospinal fluid (CSF) or blood tests to measure levels of beta-amyloida protein that accumulates in the brains of people with Alzheimer’s disease.

However, these diagnostic tests may not be accurate, have limited availability, or take a long time, during which the disease could progress without treatment.

Now a team from Imperial College London has developed a MRI-based machine learning system to quickly and accurately diagnose Alzheimer’s disease. In their study published in Communication Medicinethe method could detect both early and more advanced Alzheimer’s disease.

The researchers developed an algorithm based on those used to classify cancerous tumours. After dividing the brain into 115 regions, they assigned 660 characteristics, such as shape, size and texture, to each region. They trained the algorithm to predict Alzheimer’s disease by identifying changes in these features from a single standard MRI.

They tested their method on brain scans of more than 400 patients as part of the Alzheimer’s Disease Neuroimaging Initiative. These patients had early or advanced Alzheimer’s disease and were compared with healthy controls and patients with other neurological disorders.

They then tested it using data from 80 patients undergoing diagnostic testing for Alzheimer’s disease at Imperial College Healthcare NHS Trust.

“This new research approach uses machine learning and MRI scans to try to identify biological brain changes early in the Alzheimer’s disease continuum. That being said, this research is in its early stages and not ready to be used as a standalone diagnostic tool.

– Dr. Rebecca Edelmayer, Ph.D., Senior Director of Science Engagement, Alzheimer’s Association

The researchers found that the MRI-based machine learning system accurately predicted Alzheimer’s disease in 98% of cases in their initial study. It could also distinguish between early and advanced stages of Alzheimer’s disease in 79% of cases.

When tested on an external dataset, the algorithm still detected 86% of Alzheimer’s cases, a figure higher than previously published studies.

Dr. Anton Porsteinsson, professor and director of the Alzheimer’s Disease Care, Research and Education (AD-CARE) program at the University of Rochester Medical Center, praised their findings:

“Their method appears to be highly predictive in this population and adds to the number of imaging techniques and fluid biomarkers that make the diagnosis of dementia more accurate.”

The algorithm also showed greater accuracy than currently used measurements – hippocampal atrophy and cerebrospinal fluid (CSF) beta-amyloid measurement – ​​which show 26% and 62% accuracy, respectively.

The researchers suggest that their method of analysis and algorithm could be an alternative to invasive CSF measurements.

However, Dr. Porsteinsson said Medical News Today“There is intense exploration going on right now to find the most practical yet accurate biomarkers for diagnosis, prognosis, and possible treatment outcomes in Alzheimer’s disease and related dementias. This study suggests that the authors’ technique may find a role here, but the competition is formidable, especially fluid biomarkers.

Because the new method can detect early changes in Alzheimer’s disease, it could lead to earlier diagnosis, allowing treatments to begin before symptoms become life-changing.

Lead researcher Professor Eric Aboagye, from Imperial College’s Department of Surgery and Cancer, called their research “an important step forward”.

“Waiting for a diagnosis can be a horrifying experience for patients and their families. If we could reduce the waiting time, simplify the diagnostic process and reduce some of the uncertainty, that would help a lot,” Prof Aboagye said.

Commenting for DTMDr. Edelmayer agreed: “[T]his research focuses on an important issue in Alzheimer’s disease: early detection. With the FDA’s fast-tracking approval of the first Alzheimer’s disease-modifying amyloid therapy and more to come, it is critical that people with Alzheimer’s disease be diagnosed early in the disease process. when treatment may be most beneficial.

Biogen’s Aduhelm (aducanumab) is the treatment Dr. Edelmayer is referring to, for which the FDA has granted expedited approval in 2021.

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