AI May Detect Alzheimer's Years Before Diagnosis
An artificial intelligence (AI) algorithm that uses positron-emission tomography (PET) improves the ability of brain imaging to predict Alzheimer's disease (AD) at an early stage, new research shows. Investigators studied more than 2000 prospective 18F-fluorodeoxyglucose (18F-FDG) PET images taken from 1000 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). They trained the algorithm on 90% of the dataset and then tested it on the remaining 10%. The algorithm successfully "learned" to identify the metabolic patterns that corresponded to AD. When the algorithm was tested on an independent set of 40 images from 40 never-studied patients, it achieved 100% sensitivity at detecting the disease an average of more than 6 years prior to the final diagnosis. "The key point of our study is that our algorithm not only successfully detects AD but actually detects it 6 years before the diagnosis is made," corresponding author Jae Ho Sohn, MD, of the Department of Radiology and Biomedical Imaging, University of California, San Francisco, told Medscape Medical News. "In order to develop treatments for AD, and even for the patient's sake, it is better to know about the disease early, because by the time it is diagnosed, there is usually too much brain volume loss and little we can do to help the patient," he said. The study was published online November 6 in Radiology.
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