The devastating neurodegenerative condition Alzheimer's disease is incurable, but with early detection, patients can seek treatments to slow the disease's progression, before some major symptoms appear. Now, by applying artificial intelligence algorithms to MRI brain scans, researchers have developed a way to automatically distinguish between patients with Alzheimer's and two early forms of dementia that can be precursors to the memory-robbing disease. The researchers, from the VU University Medical Center in Amsterdam, suggest the approach could eventually allow automated screening and assisted diagnosis of various forms of dementia, particularly in centers that lack experienced neuroradiologists. Additionally, the results, published online July 6 in the journal Radiology, show that the new system was able to classify the form of dementia that patients were suffering from, using previously unseen scans, with up to 90 percent accuracy. "The potential is the possibility of screening with these techniques so people at risk can be intercepted before the disease becomes apparent," said Alle Meije Wink, a senior investigator in the center's radiology and nuclear medicine department.
Make room, stethoscope and otoscope. Artificial intelligence (AI) applications are increasingly among the physician's standard instruments,experts at the University of Toronto say. "With electronic records, you can use text algorithms to read a patient's history, review their genetic predispositions, and correlate the information to make predictions," says Dr. Frank Rudzicz. Rudicz is one of five experts exploring the issues of privacy, accuracy and accountability at The Robot Will See You Now – the Revolution in Artificial Intelligence and Medicine at U of T on April 5. A research scientist with the Toronto Rehab Institute and an assistant professor (status only) in the department of computer science at the University of Toronto, Rudzicz is also a project lead within a federally funded national research network in technology and aging known as AGE-WELL NCE.
Artificial technology has recently been viewed as an asset of precision medicine that can outsmart several diseases. With the advancement and sophistication of modern technology, artificial intelligence has seamlessly coalesced into the field of medicine. In fact, AI technology has recently been viewed as an asset of precision medicine that can outmaneuver tough medical problems. The presence of artificial intelligence in the field of medicine is nothing new. Last month, a team of scientists at the California NanoSystems Institute at UCLA has developed a new technique using artificial intelligence to efficiently detect cancer cells without damaging blood samples, as previously reported.
Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says. Machine learning is a type of artificial intelligence that allows computer programs to learn when exposed to new data without being programmed. "With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam. "But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," Meije Wink explained.
NETHERLANDS -Machine learning is a type of artificial intelligence that allows computer programs to learn things when exposed to new data, without being reprogrammed. Now, researchers have matched machine learning methods with a special technique of magnetic resonance imaging (MRI) that measures blood perfusion (absorption rate of this tissue) throughout the brain to detect early forms of dementia. MRI can help diagnose Alzheimer's disease. However, early diagnosis is difficult. Scientists have long known that Alzheimer's disease is a gradual process and that the brain undergoes functional changes before the structural changes associated with the disease visually displayed on the test results.