Machine Deep Learning Shows Ability to Spot People Who Developed Alzheimer’s Years in Advance of Diagnosis

Machine Deep Learning Shows Ability to Spot People Who Developed Alzheimer’s Years in Advance of Diagnosis
Deep learning, a type of artificial intelligence, used brain imaging scans to predict — years in advance and with nearly 100% sensitivity — patients who eventually developed Alzheimer’s disease, a study reports. The study, “A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain,” was published in the journal Radiology. A timely diagnosis of Alzheimer's disease is key for better treatment and intervention, but is often challenging. Usually, changes in metabolism identified by glucose uptake in certain brain regions can provide clinical clues during the disease's early stages. However, these changes are often difficult to identify. Advancements in imaging diagnostic technology, such as 18-F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), can allow for earlier diagnosis and treatment. In an 18F-FDG PET scan, FDG, a radioactive glucose compound, is injected directly into the blood. PET scans then measure the uptake of FDG in brain cells, which is an indicator of metabolic activity. "Differences in the pattern of glucose uptake in the brain are very subtle and diffuse," Jae Ho Sohn, MD, at the University of California in San Francisco (UCSF) and a study co-author, said in a press release. "People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process." A continuous spectrum from normal cognition to severe impairment marks Alzheimer's, and includ
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