Great Britain – A machine learning algorithm machine learning can determine if a person has Alzheimer’s disease (AD) with a 98% accuracy using a single MRI scan communication medicine .
“Currently, no other simple and widely used method can diagnose Alzheimer’s disease with this accuracy, so our research is an important step forward,” the ministry said in a statement. Professor Eric Aboagye from Imperial College London, which conducted the research.
“Many patients who come to a recall consultation with Alzheimer’s disease also have other neurological disorders, but even within this group, our system can distinguish patients with Alzheimer’s disease from those without,” he added.
A robust and reproducible tool
To develop the algorithm, Professor Aboagye and his colleagues divided the brain into 115 regions and assigned them 660 different characteristics such as size, shape and texture. They trained the algorithm to recognize exactly where changes in this or that trait might correspond to Alzheimer’s disease.
Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the team tested their algorithm on brain MRI scans of more than 400 patients with late-stage and advanced Alzheimer’s disease, healthy controls, and patients with other neurological disorders, including frontotemporal disorders Dementia and Parkinson’s.
They also tested it using data from more than 80 patients undergoing AD diagnostic tests at the Imperial College Healthcare NHS Trust.
In 98% of cases, the MRI-based machine learning tool alone was able to accurately predict whether a person had Alzheimer’s disease, outperforming traditional measurements of hippocampal volume and hippocampal beta-amyloid protein in cerebrospinal fluid (CSF). It was also able to distinguish between early and advanced stages of Alzheimer’s disease in 79% of patients.
The tool proved to be “robust and reproducible across MRI scans, demonstrating its potential for application in clinical practice in the future,” the researchers write.
“Most patients have to go through a whole series of tests before they receive a diagnosis, and this tool could provide faster diagnosis and reduce patient anxiety. Of course, the specialist can use this information to refine and modify the diagnosis,” said Prof Aboagye.
The algorithm also detected changes in areas of the brain not previously associated with Alzheimer’s disease, including the cerebellum and ventral diencephalon. This ‘opens up opportunities for researchers’ to look more closely at these areas and see how they may be linked to dementia, Professor Aboagye pointed out.
“Although neuroradiologists are already interpreting MRIs to help diagnose Alzheimer’s disease, it is likely that some features of the scans are not visible even to specialists,” he explained Pr Paresh MalhotraCo-Investigators (Imperial College London) in the press release.
‘Using an algorithm that can detect the texture and subtle structural features of the brain affected by Alzheimer’s disease could really improve the insights we can get with standard imaging techniques,’ Professor Malhotra added.
You have to repeat the experience
Speaking of Medscape Medical Newsthe dr Cyrus A RajiAssistant professor of radiology and neurology at Washington University in St. Louis, Missouri, said the study shows that “new computer analyzes of structural or T1-weighted images can identify Alzheimer’s disease with a high degree of precision.
“However, translation into clinical practice requires replication of these results as well as software optimized for the clinical environment,” concluded Dr. Raji.
This research was funded in part by Imperial College London’s NIHR Biomedical Research Center and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Prs Aboagye, Malhotra and Raji did not disclose any relevant financial relationships.
The article originally appeared on Medscape.com titled Can a Single Brain Scan Accurately Diagnose Alzheimer’s? Translated by Aude Lecrubier.
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