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Using AI to battle Alzheimer's

March 15, 2022

ASU researchers and Banner Health medical imaging expert team up to take steps toward more effective treatments for debilitating disease

It’s estimated that more than 6 million Americans — and about 24 million people worldwide — are living with the degenerative brain disease called Alzheimer’s, a progressive mental deterioration that is the fifth leading cause of death in the United States among people who are 65 and older.

The Alzheimer’s Association and other sources report that medical experts estimate the number of people with this form of dementia more than doubling within the next three decades.

That reality is leading to more research aimed at deepening knowledge about the causes and progression of the disease to help in developing effective treatments.

One essential key to success is finding ways to more accurately detect the early indicators of the disease, for example, through neuroimaging —  generating images of the brain — as well as ways to more clearly visualize its physiological signs.

Advances in neuroimaging and related medical technologies would more closely reveal the specific conditions of individual patients, providing a foundation for effective analysis on which to base diagnoses and treatment options.

Among those taking on the challenge are researchers Teresa WuYi Su and Jay Shah

Wu is a professor and Shah is a graduate research associate in the School of Computing and Augmented Intelligence, one of the seven Ira A. Fulton Schools of Engineering at Arizona State University. Su is the director of computational image analysis at the Banner Alzheimer’s Institute, or BAI, in Phoenix, a part of Banner Health.

Wu is also co-director of ASU-Mayo Center for Innovative Imaging, and Su is one of the center’s affiliate faculty members.

Wu and Su are the co-senior authors and Shah the lead author of a recently published paper in Alzheimer’s & Dementia, the journal of the Alzheimer’s Association, “Deep residual inception encoder-decoder network for amyloid PET harmonization.”

positron emission tomography images of human brains

This composite image shows a visual comparison of actual positron emission tomography, or PET, images of human brains and synthetic PET brain images generated using a network built by a team of researchers from Arizona State University and the Banner Alzheimer’s Institute. These results are important to the team’s studies aimed at improving the detection and diagnosis of Alzheimer’s disease. In this figure, amyloid PET data from two commonly used the tracers florbetapir, or FBP, and Pittsburgh Compound B, or PiB, are shown (in the second and third columns) for the same set of patients. This shows obvious differences visually between images from the two tracers. The technique developed by the research team was able to generate synthetic PiB images (in the last column) based on FBP images (in the second column), producing much improved visual similarity between the synthetic PiB images and real PiB images (in the third column). Image courtesy of Yi Su’s Banner Alzheimer’s Institute research team

The research paper describes the intricate details of leveraging deep learning techniques and amyloid imaging with the use of positron emission tomography, or PET, tracers to provide a more accurate interpretation and better quantitative analysis as a basis for Alzheimer’s detection and diagnostics.

Amyloid plaques, as described by, are aggregates of abnormally configured proteins that form in spaces between nerve cells. The proteins are thought to play a central role in the development of Alzheimer’s disease. The amyloid plaques first develop in the areas of the brain associated with memory and other cognitive functions.

The brain contains tens of billions of neurons that process and transmit information by electrical and chemical signals, sending messages between different parts of the brain, and from the brain to muscles and organs. Alzheimer’s disease disrupts communications between neurons, causing loss of body function and cell death.

In their work, Wu, Su, Shah and others on their research teams from ASU and BAI are exploring a more efficient way to analyze images produced by amyloid PET tracers, the in vivo technology used to quantify beta-amyloid deposition in the brain, which is a precursor for Alzheimer’s disease.

Multiple PET tracers are used in different research studies, and the differences between them pose a challenge for developing a consensus on accurate quantitative comparisons for Alzheimer’s diagnosis, the researchers say.  So, their work is instead aimed at “harmonizing” these PET tracers through deep learning algorithms that can be used by medical experts to quantify amyloid depositions more effectively.

“By harmonizing, we are talking about making the PET images that are obtained by using different types of tracers look more similar, and also making the quantitative measures obtained from these images more comparable,” Wu says. “The goal is to allow the different amyloid PET tracers to be used interchangeably after applying our harmonization technique, so that clinicians and researchers can make the same interpretation, no matter which tracer is used.’’

Banner Alzheimer’s Institute Senior Scientist

Yi Su

The researchers have filed for a provisional patent for the harmonization technique.

“I think this work will accelerate research so that people can better leverage the data that have already been collected or from ongoing studies using different PET tracers,” says Su, whose expertise includes neuroimaging and Alzheimer’s disease.

“It will help us better understand how Alzheimer’s disease is developed, how it progresses over time, and enable data-driven approaches that facilitate Alzheimer’s disease research,” he says. “The ultimate goal is to develop effective treatments that might slow down disease progression, prevent or even cure the disease.”

The project combines Wu’s expertise in health informatics with Su’s experience in medical imaging.

Shah, a computer scientist, is adding to the mix his knowledge of machine learning and deep learning techniques, both of which intersect with artificial intelligence concepts and technologies.

“I want to improve on the modeling used to help make advances in clinical research using the combined capabilities of both machine learning and deep learning,” Shah says.

By doing that, the researchers hope to take steps toward expanding methods they have specifically designed to reveal the signs and progression of Alzheimer’s disease.

Fulton Schools Graduate Research Assistant and computer scientist

Jay Shah

The project seems likely to lead to other patents for new techniques and methodologies the team is developing, as well as potential commercialization of the specific tools the researchers might create to perform those methods and techniques.

Progress on that front would open possibilities for support of their work from the federal Small Business Innovation Research and Small Business Technology Transfer programs, Wu says.

The research currently has funding from the Arizona Department of Health Services and from several National Institutes of Health, or NIH, grants. With advances toward their goal, Wu, Su and Shah hope to attract additional NIH support.

Wu emphasizes that whatever transpires on the commercialization and funding fronts, she and her colleagues want to ensure that the tools and knowledge they produce will be made available for use in further studies by medical researchers and to educate students.

“We want to put the results of what we are discovering into the hands of experts in many areas of science and engineering research and development,” Wu says. “That’s what will be needed to win the battle against Alzheimer’s.”

Top photo: Teresa Wu (at left in foreground) is a professor in the industrial engineering program in the Ira A. Fulton Schools of Engineering at Arizona State University. Her expertise includes health informatics, which involves acquiring, studying and managing health data, and applying medical concepts along with health information technology systems to help clinicians provide effective healthcare. Photo by Pete Zrioka/ASU

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Food prices will continue to rise, likely through next year

March 15, 2022

W. P. Carey School expert explains all the factors causing prices to go up at the grocery store

Inflation reminders are everywhere these days. In conversations with friends, on social media, while driving to or past a gas station. And yes, at the grocery store.

Nobody likes to pay more for food, that’s clear. But unfortunately, the world has not escaped all of the pandemic-related challenges just yet. And now there’s a war in Ukraine, which is only adding to the global impact.

On March 10, inflation hit 7.9% — a new 40-year high. And according to the U.S. Department of Agriculture’s most recent Food Price Outlook, the cost for all foods could go up anywhere from 2.5%–3.5% this year. When will it get better? Not anytime soon, apparently, which is why ASU News spoke with Tim Richards, the Morrison Chair in Agribusiness in the W. P. Carey School of Business at Arizona State University, to help put the story about rising food prices into context.

, Morrison Chair in Agribusiness in the W. P. Carey School of Business at Arizona State University

Tim Richards

Question: We’ve heard a lot about global food prices being impacted by the war in Ukraine, especially for exports like wheat. Will the U.S. also feel the impact?

Answer: Ukraine, historically, is known as the “breadbasket of Europe.” It has some of the most fertile soil because of the climate, which makes it a fantastic area for agriculture. When it comes to the global wheat market, 25% of all wheat exports are through Ukraine, making it an important contributor to the world economy. The global market for grains and oil seeds, which includes wheat and barley and corn and soy, is fully integrated. Ukraine is also the world’s largest producer of sunflower, so there will be a direct impact on imports of sunflower and sunflower oil in the U.S. The whole market works together. So ultimately, if you remove one source of supply, that’s what’s going to cause global prices to increase in general.

Q: What’s behind rising food prices? Is it one thing contributing to the problem or multiple factors?

A: It’s a perfect storm of things going on right now. COVID-19, our policy responses to COVID-19, the injection of trillions of dollars into the economy — an unprecedented scale of fiscal stimulus. We really didn’t know what was going to happen to demand. Turns out, consumers are still spending like you wouldn’t believe; they’re just buying more stuff. Additional spending, for so-called luxury products, constrains how we move food to market. Food producers are competing with other companies for scarce supply chain services like cargo ships or trucks to move products consumers are demanding. For example, a truck moving a motorbike from Los Angeles to Kansas could have also moved food. But trucking costs have risen sharply. A refrigerated truck for fresh produce traveling from Los Angeles to New York costs 40% more compared to pre-pandemic times. Same goes for cargo ships. The cost for a ship traveling from China to Los Angeles would have cost about $2,000. This year, the cost is more like $13,000–14,000.

Q: What can we expect in the coming months? Will food prices continue to go up?

A: There are two components of cost that are absolutely essential to virtually everything that’s produced: fuel and labor. With oil prices now up higher than they were in 2008, eventually that’s going to cut into firms’ (processing firms, food retailers, farmers) bottom lines. Fuel, hydrocarbons in general, are a core cost to everything that we produce. Trucking products to people’s doorsteps takes fuel. The machines farmers use to grow crops takes fuel. Most fertilizers that farmers use are natural gas based, and right now, natural gas prices are through the roof, being driven up higher by the war between Russia and Ukraine. So that’s one side. Labor is another. There’s a shortage of labor in virtually every industry you can care to talk about.

We’re (the W. P. Carey School of Business) doing an analysis of the wage component of food costs, and there are some estimates that farm labor costs are going to go up by 25% between last year and next year. Firms will have to bid up wages to get the number of workers that they need to do the work. So, yes, food prices will go up. All of these cost increases are just starting. For instance, farmers buy fertilizer about a year ahead, which means the high natural gas prices won’t be baked into fertilizer prices until probably this harvest season into next year. The labor component hasn’t been baked in yet either. It’s an ongoing thing, and as long as those pressures are there, it’s going to keep on running.

Q: Can consumers expect any tiny bit of relief?

A: On the bright side, the record amount of snow in California’s Sierra Nevada Mountains is a good thing because all of that water will be used to irrigate crops in California. During the droughts in the state in 2013 and 2014, California saw record prices for fresh produce. We won’t have a lack of supply due to a lack of water pushing prices up, although we’ll still have the labor component for some of those crops. But California having water is really a big deal when it comes to fresh food prices, especially in Arizona.

Top image courtesy of

Jimena Garrison

Media Relations Officer , Media Relations and Strategic Communications