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:

Scientists predict the areas of the brain to stimulate transitions between different brain states
Using a computer model of the brain, Gustavo Deco, director of the Center for Brain and Cognition, and Josephine Cruzat, a member of his team, together with a group of international collaborators, have developed an innovative method published in Proceedings of the National Academy of Sciences on Sept.
BRAIN Initiative tool may transform how scientists study brain structure and function
Researchers have developed a high-tech support system that can keep a large mammalian brain from rapidly decomposing in the hours after death, enabling study of certain molecular and cellular functions.
Wiring diagram of the brain provides a clearer picture of brain scan data
In a study published today in the journal BRAIN, neuroscientists led by Michael D.
Blue Brain Project releases first-ever digital 3D brain cell atlas
The Blue Brain Cell Atlas is like ''going from hand-drawn maps to Google Earth'' -- providing previously unavailable information on major cell types, numbers and positions in all 737 brain regions.
Landmark study reveals no benefit to costly and risky brain cooling after brain injury
A landmark study, led by Monash University researchers, has definitively found that the practice of cooling the body and brain in patients who have recently received a severe traumatic brain injury, has no impact on the patient's long-term outcome.
Brain cells called astrocytes have unexpected role in brain 'plasticity'
Researchers from the Salk Institute have shown that astrocytes -- long-overlooked supportive cells in the brain -- help to enable the brain's plasticity, a new role for astrocytes that was not previously known.
Largest brain study of 62,454 scans identifies drivers of brain aging
In the largest known brain imaging study, scientists from Amen Clinics (Costa Mesa, CA), Google, John's Hopkins University, University of California, Los Angeles and the University of California, San Francisco evaluated 62,454 brain SPECT (single photon emission computed tomography) scans of more than 30,000 individuals from 9 months old to 105 years of age to investigate factors that accelerate brain aging.
Is whole-brain radiation still best for brain metastases from small-cell lung cancer?
University of Colorado Cancer Center study compares outcomes of 5,752 small-cell lung cancer patients who received whole-brain radiation therapy (WBRT) with those of 200 patients who received stereotactic radiosurgery (SRS), finding that the median overall survival was actually longer with SRS (10.8 months with SRS versus 7.1 months with WBRT).
Atlas of brain blood vessels provides fresh clues to brain diseases
Even though diseases of the brain vasculature are some of the most common causes of death in the West, knowledge of these blood vessels is limited.
Brain sciences researcher pinpoints brain circuit that triggers fear relapse
Steve Maren, the Claude H. Everett Jr. '47 Chair of Liberal Arts professor in the Department of Psychological and Brain Sciences at Texas A&M University, and his Emotion and Memory Systems Laboratory (EMSL) have made a breakthrough discovery in the process of fear relapse.
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

Rethinking Anger
Anger is universal and complex: it can be quiet, festering, justified, vengeful, and destructive. This hour, TED speakers explore the many sides of anger, why we need it, and who's allowed to feel it. Guests include psychologists Ryan Martin and Russell Kolts, writer Soraya Chemaly, former talk radio host Lisa Fritsch, and business professor Dan Moshavi.
Now Playing: Science for the People

#538 Nobels and Astrophysics
This week we start with this year's physics Nobel Prize awarded to Jim Peebles, Michel Mayor, and Didier Queloz and finish with a discussion of the Nobel Prizes as a way to award and highlight important science. Are they still relevant? When science breakthroughs are built on the backs of hundreds -- and sometimes thousands -- of people's hard work, how do you pick just three to highlight? Join host Rachelle Saunders and astrophysicist, author, and science communicator Ethan Siegel for their chat about astrophysics and Nobel Prizes.