Nav: Home

Artificial intelligence predicts Alzheimer's years before diagnosis

November 06, 2018

OAK BROOK, Ill. - Artificial intelligence (AI) technology improves the ability of brain imaging to predict Alzheimer's disease, according to a study published in the journal Radiology.

Timely diagnosis of Alzheimer's disease is extremely important, as treatments and interventions are more effective early in the course of the disease. However, early diagnosis has proven to be challenging. Research has linked the disease process to changes in metabolism, as shown by glucose uptake in certain regions of the brain, but these changes can be difficult to recognize.

"Differences in the pattern of glucose uptake in the brain are very subtle and diffuse," said study co-author Jae Ho Sohn, M.D., from the Radiology & Biomedical Imaging Department at the University of California in San Francisco (UCSF). "People are good at finding specific biomarkers of disease, but metabolic changes represent a more global and subtle process."

The study's senior author, Benjamin Franc, M.D., from UCSF, approached Dr. Sohn and University of California, Berkeley, undergraduate student Yiming Ding through the Big Data in Radiology (BDRAD) research group, a multidisciplinary team of physicians and engineers focusing on radiological data science. Dr. Franc was interested in applying deep learning, a type of AI in which machines learn by example much like humans do, to find changes in brain metabolism predictive of Alzheimer's disease.

The researchers trained the deep learning algorithm on a special imaging technology known as 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET). In an FDG-PET scan, FDG, a radioactive glucose compound, is injected into the blood. PET scans can then measure the uptake of FDG in brain cells, an indicator of metabolic activity.

The researchers had access to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a major multi-site study focused on clinical trials to improve prevention and treatment of this disease. The ADNI dataset included more than 2,100 FDG-PET brain images from 1,002 patients. Researchers trained the deep learning algorithm on 90 percent of the dataset and then tested it on the remaining 10 percent of the dataset. Through deep learning, the algorithm was able to teach itself metabolic patterns that corresponded to Alzheimer's disease.

Finally, the researchers tested the algorithm on an independent set of 40 imaging exams from 40 patients that it had never studied. The algorithm achieved 100 percent sensitivity at detecting the disease an average of more than six years prior to the final diagnosis.

"We were very pleased with the algorithm's performance," Dr. Sohn said. "It was able to predict every single case that advanced to Alzheimer's disease."

Although he cautioned that their independent test set was small and needs further validation with a larger multi-institutional prospective study, Dr. Sohn said that the algorithm could be a useful tool to complement the work of radiologists--especially in conjunction with other biochemical and imaging tests--in providing an opportunity for early therapeutic intervention.

"If we diagnose Alzheimer's disease when all the symptoms have manifested, the brain volume loss is so significant that it's too late to intervene," he said. "If we can detect it earlier, that's an opportunity for investigators to potentially find better ways to slow down or even halt the disease process."

Future research directions include training the deep learning algorithm to look for patterns associated with the accumulation of beta-amyloid and tau proteins, abnormal protein clumps and tangles in the brain that are markers specific to Alzheimer's disease, according to UCSF's Youngho Seo, Ph.D., who served as one of the faculty advisors of the study.

"If FDG-PET with AI can predict Alzheimer's disease this early, beta-amyloid plaque and tau protein PET imaging can possibly add another dimension of important predictive power," he said.
-end-
"A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease Using 18F-FDG PET of the Brain." Collaborating with Drs. Sohn, Franc, and Seo and Ms. Ding were Michael G. Kawczynski, M.S., Hari Trivedi, M.D., Roy Harnish, M.S., Nathaniel W. Jenkins, M.S., Dmytro Lituiev, Ph.D., Timothy P. Copeland, M.P.P., Mariam S. Aboian, M.D., Ph.D., Carina Mari Aparici, M.D., Spencer C. Behr, M.D., Robert R. Flavell, M.D., Ph.D., Shih-Ying Huang, Ph.D., Kelly A. Zalocusky, Ph.D., Lorenzo Nardo, Ph.D., Randall A. Hawkins, M.D., Ph.D., Miguel Hernandez Pampaloni, M.D., Ph.D., and Dexter Hadley, M.D., Ph.D.

Radiology is edited by David A. Bluemke, M.D., Ph.D., University of Wisconsin School of Medicine and Public Health, Madison, Wis., and owned and published by the Radiological Society of North America, Inc.

RSNA is an association of over 54,000 radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Ill. (RSNA.org)

For patient-friendly information on FDG-PET, visit RadiologyInfo.org.

Radiological Society of North America

Related Brain Articles:

Study describes changes to structural brain networks after radiotherapy for brain tumors
Researchers compared the thickness of brain cortex in patients with brain tumors before and after radiation therapy was applied and found significant dose-dependent changes in the structural properties of cortical neural networks, at both the local and global level.
Blue Brain team discovers a multi-dimensional universe in brain networks
Using a sophisticated type of mathematics in a way that it has never been used before in neuroscience, a team from the Blue Brain Project has uncovered a universe of multi-dimensional geometrical structures and spaces within the networks of the brain.
New brain mapping tool produces higher resolution data during brain surgery
Researchers have developed a new device to map the brain during surgery and distinguish between healthy and diseased tissues.
Newborn baby brain scans will help scientists track brain development
Scientists have today published ground-breaking scans of newborn babies' brains which researchers from all over the world can download and use to study how the human brain develops.
New test may quickly identify mild traumatic brain injury with underlying brain damage
A new test using peripheral vision reaction time could lead to earlier diagnosis and more effective treatment of mild traumatic brain injury, often referred to as a concussion.
More Brain News and Brain Current Events

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Teaching For Better Humans
More than test scores or good grades — what do kids need to prepare them for the future? This hour, guest host Manoush Zomorodi and TED speakers explore how to help children grow into better humans, in and out of the classroom. Guests include educators Olympia Della Flora and Liz Kleinrock, psychologist Thomas Curran, and writer Jacqueline Woodson.
Now Playing: Science for the People

#535 Superior
Apologies for the delay getting this week's episode out! A technical glitch slowed us down, but all is once again well. This week, we look at the often troubling intertwining of science and race: its long history, its ability to persist even during periods of disrepute, and the current forms it takes as it resurfaces, leveraging the internet and nationalism to buoy itself. We speak with Angela Saini, independent journalist and author of the new book "Superior: The Return of Race Science", about where race science went and how it's coming back.