Featured Post from University of Florida

AI-driven software is 96% accurate at diagnosing Parkinson's

Dec 30, 2025

3 min

Existing research indicates that the accuracy of a Parkinson’s disease diagnosis hovers between 55% and 78% in the first five years of assessment. That’s partly because Parkinson’s sibling movement disorders share similarities, sometimes making a definitive diagnosis initially difficult.

Although Parkinson’s disease is a well-recognized illness, the term can refer to a variety of conditions, ranging from idiopathic Parkinson’s, the most common type, to other movement disorders like multiple system atrophy Parkinsonian variant and progressive supranuclear palsy. Each shares motor and nonmotor features, like changes in gait — but possess a distinct pathology and prognosis.

Roughly one in four patients, or even one in two patients, is misdiagnosed.

Now, researchers at the University of Florida and the UF Health Norman Fixel Institute for Neurological Diseases have developed a new kind of software that will help clinicians differentially diagnose Parkinson’s disease and related conditions, reducing diagnostic time and increasing precision beyond 96%. The study was published recently in JAMA Neurology and was funded by the National Institutes of Health.

“In many cases, MRI manufacturers don’t communicate with each other due to marketplace competition,” said David Vaillancourt, Ph.D., chair and a professor in the UF Department of Applied Physiology and Kinesiology. “They all have their own software and their own sequences. Here, we’ve developed novel software that works across all of them.”

Although there is no substitute for the human element of diagnosis, even the most experienced physicians who specialize in movement disorder diagnoses can benefit from a tool to increase diagnostic efficacy between different disorders, Vaillancourt said.

The software, Automated Imaging Differentiation for Parkinsonism, or AIDP, is an automated MRI processing and machine learning software that features a noninvasive biomarker technique. Using diffusion-weighted MRI, which measures how water molecules diffuse in the brain, the team can identify where neurodegeneration is occurring. Then, the machine learning algorithm, rigorously tested against in-person clinic diagnoses, analyzes the brain scan and provides the clinician with the results, indicating one of the different types of Parkinson’s.

The study was conducted across 21 sites, 19 of them in the United States and two in Canada.

“This is an instance where the innovation between technology and artificial intelligence has been proven to enhance diagnostic precision, allowing us the opportunity to further improve treatment for patients with Parkinson’s disease,” said Michael Okun, M.D., medical adviser to the Parkinson’s Foundation and director of the Norman Fixel Institute for Neurological Diseases at UF Health. “We look forward to seeing how this innovation can further impact the Parkinson’s community and advance our shared goal of better outcomes for all.”

The team’s next step is obtaining approval from the U.S. Food and Drug Administration.

“This effort truly highlights the importance of interdisciplinary collaboration,” said Angelos Barmpoutis, Ph.D., a professor at the Digital Worlds Institute at UF. “Thanks to the combined medical expertise, scientific expertise and technological expertise, we were able to accomplish a goal that will change the lives of countless individuals.”

Vaillancourt and Barmpoutis are partial owners of a company called Neuropacs whose goal is to bring this software forward, improving both patient care and clinical trials where it might be used.

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Michael Okun

Distinguished Professor | Director

Dr. Michael Okun is a neurologist, neuroscientist, and expert in Parkinson's, movement disorders, and deep brain stimulation.

Alzheimer'sParkinson'sTouretteDeep Brain StimulationDystonia

David Vaillancourt

Professor | Chair

David Vaillancourt’s studies how the brain regulates voluntary and involuntary movement with a specific focus on motor disorders.

Structural and Functional Imaging in Rodents and HumansRehabilitative, Surgical and Pharmacological Interventions for Motor DisordersCortical Oscillations that Underlie Voluntary and Involuntary MovementNeuroscience and the BrainProgression Markers of Parkinson’s Disease

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Apr 1, 2026

3 min

UF develops breakthrough magnet that could transform metal production

Imagine if producing steel parts for agricultural equipment or even aluminum soda cans required only a fraction of the energy it does today. A University of Florida-led innovation may soon make this a reality. In a groundbreaking collaboration backed by a nearly $11 million federal grant, UF researchers have developed a first-of-its kind superconducting magnet that could advance metal production and position the United States as a global leader in alloy production.   “This revolutionary technology has the potential to substantially reduce the cost and energy use of heat treatments in the steel industry, and we are excited to help pave the way for its adoption in industry.” —Michael Tonks, Ph.D., UF’s interim chair of Materials Science and Engineering Funded by the U.S. Department of Energy’s Advanced Manufacturing Office, the project uses Induction-Coupled Thermomagnetic Processing, or ITMP, an advanced manufacturing method that integrates magnetic fields with high-temperature thermal processing. The national consortium of industry, academic and national laboratory partners is now led by Michael Tonks, Ph.D., UF’s interim chair of Materials Science and Engineering, who succeeded Michele Manuel, Ph.D., the project’s long-time leader. “This revolutionary technology has the potential to substantially reduce the cost and energy use of heat treatments in the steel industry, and we are excited to help pave the way for its adoption in industry,” said Tonks. It’s not just any piece of equipment; it’s a custom-built superconducting magnet with a unique ability to combine magnetic fields with high-temperature thermal processing. In partnership with the UF physics department, Oak Ridge National Laboratory, or ORNL, and six companies interested in the technology, the magnet and cylinder induction furnace now sit atop a 6-foot-high platform. The prototype, which costs more than $6 million to purchase and install, is capable of processing steel samples up to 5 inches in diameter making it a rare asset for academic research. Yang Yang, Ph.D., UF materials science research faculty member, estimated ITMP could reduce steel processing time by as much as 80 percent, cutting energy use and operational costs. “Thermomagnetic processing changes a material’s phase stability and kinetic properties, accelerating carbon diffusion in steel, said Yang. “Traditional furnaces cannot achieve these advanced material properties.” The system works by modifying the driving forces for important steel phase changes, which shortens heat treatment. “What normally takes eight hours can be done in just a few minutes.” Yang explained. “The magnetic field acts as an external driving force to make atoms diffuse faster.” Unlike conventional energy sources like electricity or natural gas, the ITMP process uses volumetric induction heating along with high-static magnetic fields to lower energy consumption. The project is still in a pilot phase and requires additional research and testing. At ORNL, researchers emphasized the rarity of UF’s prototype, citing its unprecedented combination of magnetic field strength and ability to process large samples and components. “This could significantly advance U.S. manufacturing and process efficiency for heat treatment of materials such as metal alloys of steel or aluminum,” said Michael Kesler, Ph.D., ORNL research scientist and lead collaborator. Kesler noted successful implementation of this technology could contribute to a reliable energy grid and more efficient industrial electrification. UF researchers contend it could also reduce carbon emissions, supporting cleaner, more sustainable manufacturing processes. The tall, two-level magnet now resides in the Powell Family Structures and Materials Laboratory on UF's East Campus. MSE plans to officially unveil it in December, inviting representatives from national labs, industry and academia. While Engineering students will have future opportunities to use it for research and experiential learning, UF researchers are optimistic about potential industry adoption for industrial manufacturing in the next five to 10 years. The award is part of a $187 million DOE initiative to strengthen competitiveness in U.S. manufacturing. If successful, the innovation could redefine how the world shapes the materials of tomorrow.

Mar 30, 2026

3 min

UF researchers aim to improve nutrition for cancer patients

A new study and first-of-its-kind food pharmacy at UF aim to help patients with cancer access and eat nutritious foods, giving them the best possible shot at a healthy future. As many as a third of cancer patients face food challenges, particularly in rural areas. Good nutrition can improve outcomes during and after treatment. With a grant from the Florida Department of Health, a team of researchers at the University of Florida Health Cancer Center and Sylvester Comprehensive Cancer Center will first assess the community’s nutritional needs. Then they’ll test the usefulness of a food-focused digital tool designed to connect patients to helpful resources. An on-site food pharmacy will help patients not only get the food they need to thrive but also provide tools for lasting change. “We’re taking a community-based approach to holistic cancer care,” said Dejana Braithwaite, Ph.D., associate director for population sciences at the UF Health Cancer Center. “Patients consistently express that nutrition is an important issue for them during cancer treatment. We want to address nutritional needs from treatment through survivorship with a sustainable intervention. ASCENT brings science and community together to make that a reality in Florida.” Braithwaite, a professor and division chief in the UF Department of Surgery, is leading the multi-institution study with Tracy Crane, Ph.D., R.D.N., director of lifestyle medicine, prevention and digital health and co-lead of the Cancer Control Program at Sylvester, part of the University of Miami Health System. Researchers from the UF colleges of Journalism, Medicine, and Public Health and Health Professions and UF/IFAS Extension are participating. The Florida Partnership for Adding Social Context to Address Cancer Survivorship Outcomes study, which the researchers have nicknamed ASCENT, will focus on those affected by the most prevalent cancers in Florida, including breast, lung, colorectal, prostate and blood cancers. “Cancer survivors who follow a healthy dietary pattern have a lower risk of recurrence and death,” said Cora Best, Ph.D., R.D.N., an assistant professor of nutritional sciences in the UF College of Agricultural and Life Sciences and study team member. “Some cancer therapies have long-term or late side effects that increase the risk for chronic conditions, like osteoporosis, which can be alleviated with good nutrition. That means a healthy diet during and after oncologic treatment can enhance lifespan and quality of life.” Researchers will start by conducting interviews with patients, providers and community-based organizations. They want to understand how to best use resources to meet the nutritional needs of those with cancer, such as food security and diet quality. “Community outreach and engagement with various groups is a cornerstone of the study,” said Francis Dalisay, Ph.D., an associate professor in the UF College of Journalism and Communications who helped develop the interview guides. The team will use the information to build a diet intervention with online surveys and patient navigator support, which they will test in a randomized clinical study at UF Health and Sylvester. Patient navigators will connect patients with resources like community programs or specialist referrals. The food pharmacy, located at the UF Clinical and Translational Sciences Metabolic Kitchen, will help cancer patients get healthy, whole nutrient-dense foods like high-protein items, fruits, vegetables and pantry staples. It will also provide workshops, personalized recipes and meal plans. Although the United States is a wealthy nation, food insecurity remains common, including in Florida, Best said. “The ASCENT study pairs evidence-based dietary guidance for cancer survivors with innovative strategies to overcome barriers like food insecurity,” she said. Ultimately, the study aims to empower patients so they can address lifestyle factors in their control, boosting their well-being. “I am hopeful this study will provide patients with appropriate resources to improve their overall nutrition, especially those who are malnourished,” said Paul Crispen, M.D., the Cancer Center’s associate director for clinical research and a study adviser.

Mar 27, 2026

3 min

New light-based chip boosts power efficiency of AI tasks 100 fold

A team of engineers has developed a new kind of computer chip that uses light instead of electricity to perform one of the most power-intensive parts of artificial intelligence — image recognition and similar pattern-finding tasks. Using light dramatically cuts the power needed to perform these tasks, with efficiency 10 or even 100 times that of current chips performing the same calculations. Using this approach could help rein in the enormous demand for electricity that is straining power grids and enable higher performance AI models and systems. This machine learning task, called “convolution,” is at the heart of how AI systems process pictures, videos and even language. Convolution operations currently require large amounts of computing resources and time. These new chips, though, use lasers and microscopic lenses fabricated onto circuit boards to perform convolutions with far less power and at faster speeds. In tests, the new chip successfully classified handwritten digits with about 98% accuracy, on par with traditional chips “Performing a key machine learning computation at near zero energy is a leap forward for future AI systems,” said study leader Volker J. Sorger, Ph.D., the Rhines Endowed Professor in Semiconductor Photonics at the University of Florida. “This is critical to keep scaling up AI capabilities in years to come.” “This is the first time anyone has put this type of optical computation on a chip and applied it to an AI neural network,” said Hangbo Yang, Ph.D., a research associate professor in Sorger’s group at UF and co-author of the study. Sorger’s team collaborated with researchers at UF’s Florida Semiconductor Institute, the University of California, Los Angeles and George Washington University on study. The team published their findings, which were supported by the Office of Naval Research, Sept. 8 in the journal Advanced Photonics The prototype chip uses two sets of miniature Fresnel lenses using standard manufacturing processes. These two-dimensional versions of the same lenses found in lighthouses are just a fraction of the width of a human hair. Machine learning data, such as from an image or other pattern-recognition tasks, are converted into laser light on-chip and passed through the lenses. The results are then converted back into a digital signal to complete the AI task. This lens-based convolution system is not only more computationally efficient, but it also reduces the computing time. Using light instead of electricity has other benefits, too. Sorger’s group designed a chip that could use different colored lasers to process multiple data streams in parallel. “We can have multiple wavelengths, or colors, of light passing through the lens at the same time,” Yang said. “That’s a key advantage of photonics.” Chip manufacturers, such as industry leader NVIDIA, already incorporate optical elements into other parts of their AI systems, which could make the addition of convolution lenses more seamless. “In the near future, chip-based optics will become a key part of every AI chip we use daily,” said Sorger, who is also deputy director for strategic initiatives at the Florida Semiconductor Institute. “And optical AI computing is next.”