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CLEVELAND, Ohio -- Locating the source of an epileptic seizure can be tricky. Even the most advanced MRI cant pinpoint lesions, scars or other abnormalities on the brain in one-quarter of epilepsy patients.

Cleveland Clinic experts are turning to artificial intelligence to help bridge the gap.

Neurologists and brain surgeons from the Clinics Epilepsy Center are using AI and advanced medical imaging techniques to help locate the source of a patients seizures. That gives surgeons a better chance of removing any brain tissue thats associated with those seizures, which could help the patient live seizure-free for years.

The use of AI has already helped the Clinic improve the odds a surgery will result in a patient living without seizures, said Dr. Imad Najm, the director of the Epilepsy Center at the Cleveland Clinic Neurological Institute.

Every 100 patients we manage this way, 15 or 20 of them now are seizure-free [when] otherwise they would not have been if we had not applied this [technology], Najm said.

As part of Brain Awareness Week, cleveland.com is highlighting some of the advanced technology being used in brain surgery at each of Clevelands three largest health systems. The three-day series will also focus on the use of virtual reality at MetroHealth and robotics at University Hospitals.

The Clinic has been at the forefront of studying the use of AI in health care. The health system established its Center its Clinical Artificial Intelligence in 2019, and CEO Tomislav Mihaljevic has said the technology could be key to the future of health care. On Tuesday, the Clinic announced a partnership with the NFL Players Association to use AI and machine learning to diagnose and treat neurological disease.

An estimated 3.4 million people in the U.S. have epilepsy, and many of them suffer from seizures that could happen at any time. There are medications that can help, but a U.S. Centers for Disease Control and Prevention study found they prevented seizures in just 44% of patients. Those medications can also cause side effects such as fatigue, dizziness or blurred vision.

These patients whose seizures are not controlled because none of the medications work, they need some other way to help them, Najm said.

Epileptic seizures are caused by abnormal electrical activity in the brain, so neurologists need to locate the source of that abnormal activity. If they cant find any abnormalities on an MRI, they can still use other techniques to find the general area the seizures are originating. But theres only a 35 to 40% chance of success in those cases, Najm said.

With these patients we struggle immensely, Najm said. And a big chunk of them we dont do anything, and these patients will go on to live their lives with complications from seizures.

Thats where AI can help.

Artificial intelligence refers to a collection of technologies, but machine learning is one of the most common used in health care. Machine learning uses algorithms to find patterns in large amounts of data, and it can use those patterns to make predictions. For example, Spotify uses machine learning to suggest a new song you might like based on your listening history.

Clinic experts used an algorithm developed by a physicist in Switzerland and adapted it so it could help epilepsy patients. The algorithm analyzes a large amount of patient data and uses that data to predict the location of the brain tissue associated with the patients epileptic seizures.

Experts can then go back and check a patients MRI to confirm if theres an abnormality they couldnt see with the naked eye, Najm said.

Just to see something is the single most important step for evaluating someone for epilepsy surgery, Najm said.

Helping to locate the source of a seizure could be just the beginning of AIs usefulness in helping epilepsy patients, Najm said. The Clinic is currently studying magnetic resonance (MR) fingerprinting, a technology developed by biomedical engineers at Case Western Reserve University. The technology analyzes an MRI and assigns a value that could help physicians identify the cause of a patients disease or illness. Najm hopes it could eventually help determine the cause of epileptic seizures and plan a patients treatment or surgery.

Its difficult to say when that type of technology could be used in a clinical setting, because progress has been incremental, Najm said. But hes optimistic it could be the next step forward to help epilepsy patients.

In the field of difficult-to-treat epilepsies, this is very exciting, Najm said. The field of epilepsy surgery has made major advances over the last 80 years. But this new ability for us to see something we previously were not seeing has been a major step forward in the field of epilepsy surgery.

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Artificial intelligence is helping the Cleveland Clinic improve the odds an epilepsy patient can live seizure - cleveland.com

CLEVELAND, Ohio The Cleveland Clinic and the NFL Players Association are working together to use artificial intelligence and machine learning to improve the diagnosis of neurological diseases and guide treatment, the health system and players union announced Tuesday.

The joint initiative seeks to use technology to identify neurological diseases like Parkinsons and Alzheimers and determine how they might progress. The goal is to use artificial intelligence and machine learning to inform interventions and treatment, said principal investigator Dr. Jay Alberts from the Clinics Lerner Research Institute. Alberts is also vice chair for innovation for the Clinics Neurological Institute.

The opportunity we have here is that were going to be looking at a tremendous amount of data, Alberts said. By looking at all of that data and then using machine learning we are going to be able to work together and create these interesting, impactful models.

The Clinic/NFLPA initiative will use data from 60,000 of the Clinics neurological patients, who will remain anonymous, to develop algorithms related to cognitive impairment. The goal is to use the technology and a patients short-term clinical data to predict their long-term outlook.

The research will provide insight for prevention and treatment programs for current and former football players, the news release says.

This partnership with Cleveland Clinic is an exciting extension of our unions ongoing commitment to advancing the physical and mental health of our player members, NFLPA Executive Director DeMaurice Smith said in the news release. As the physician community learns more about neurological disease through the resulting clinical decision-support tools, the better informed we will be in providing education and safety initiatives for professional football players.

Artificial intelligence refers to a collection of technologies, and experts believe they have the potential to improve health care. One type of AI called machine learning uses algorithms to find patterns in large amounts of data. It can use those patterns to make predictions -- for example, Spotify uses machine learning to recommend a song you might like based on other songs youve listened to previously.

That could mean better treatment for patients. For example, Clinic researchers are currently studying whether aerobic exercise could slow the progression of Parkinsons. If AI and machine learning could help diagnose Parkinsons early on, physicians could prescribe a specific exercise regimen to help the patient, Alberts said.

We can actually start using different interventions or different approaches, and be much more prescriptive, Alberts said.

The partners also envision the models being used to help provide better health care in rural and underserved communities. A doctor in a rural area may see only three or four Parkinsons patients, but the project could provide valuable insights into how to treat them, Alberts said.

The Clinic and NFLPA also plan to work with other partners to create a research network to evaluate and create phased research projects. The network will publish any request for proposals for further projects that could further advance the understanding of neurological diseases and their progression.

Were hoping we can attract others universities, or even startups or established companies, to come in and work with us on data sets, or even bring new data to the table, and think about how we can create better and stronger models, Alberts said.

For years, studies have focused on the risk of neurological disease that football players face through brain injuries such as concussions. A 2012 study from the U.S. Centers for Disease Control and Prevention found NFL players are at a higher risk of death from brain diseases like Parkinsons, Alzheimers and ALS.

The effort is not focusing on chronic traumatic encephalopathy (CTE), the degenerative brain disease that has been linked to brain injuries that occur in football and other contact sports. That is due to the fact there is less available data on CTE, and AI and machine learning improve with large amounts of data, Alberts said.

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Cleveland Clinic, NFLPA to use artificial intelligence to improve diagnosis and treatment of neurological dis - cleveland.com

A team of Rochester Institute of Technology researchers that develops artificial intelligence systems capable of learning over time is putting its work to a unique new test: creating machines capable of playing the popular video game Starcraft II. While tasking artificial intelligence with playing a science fiction strategy game may seem odd at first glance, researchers think it could be an important stepping stone to advancing practical solutions such as self-driving cars, service robots, and other real-world applications.

Christopher Kanan, an assistant professor at RITs Chester F. Carlson Center for Imaging Science, received nearly $210,000 from the Defense Advanced Research Projects Agency (DARPA) to work on phase two of the Lifelong Learning Machines (L2M) program this year. After having success with their research in phase one of the L2M, Kanan and his team were selected to work on this new project led by SRI International and includes collaborators from American University and Georgia Tech.

: A. Sue Weisler

Assistant Professor Christopher Kanan, left, and imaging science Ph.D. student Tyler Hayes, right, discuss artificial neural networks in this photo taken in 2018.

Kanan specializes in artificial neural networkscomputing systems inspired by the biological neural networks that make up human brains which have distinct advantages over many of the artificial intelligence systems used today. Current systems are typically built using training sets to master tasks and deployed to perform that task in perpetuity, but cannot learn new tasks on the fly without suffering from a problem called catastrophic forgetting. Kanan has built systems that can learn more organically by mimicking elements of the human brain, which will be helpful for the task at hand.

The system needs to learn how to play the game and retain information about skills it learns along the way, said Kanan. One thing that we are responsible for integrating into SRIs system is teaching it to learn very quickly to avoid bad outcomes. Our human brains have a region called the amygdala thats specifically responsible for learning fear, so we are trying to integrate specific modules for learning aversion into the system.

Kanan said another benefit of the neural networks he is developing is that they are far more computationally efficient. Many current artificial intelligence systems require vast amounts of computing power and electricity to operate, placing a toll on budgets and the environment.

The grant from DARPA will help fund three RIT graduate students to work on the project over the next year, including Tyler Hayes, an imaging science Ph.D. student from Buffalo, N.Y. She said she is excited to work on the project because catastrophic forgetting presents a major obstacle for artificial intelligence systems and the team is pioneering novel approaches to solve the problem.

I think its an important problem we need to be thinking about, especially in todays day and age, said Hayes. Traditional networks that need to be trained offline and cache all the data in a server might be OK in some applications, but when you deploy a lot of these systems in real-time, you want them to be able to adapt to their environment and change. Im excited that our lab is working on some of the biggest current challenges for continual learning including image classification, object detection, and visual question answering. Its going to make these systems much more applicable in real world scenarios.

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RIT researchers helping to develop artificial intelligence systems capable of playing 'Starcraft II' | RIT - RIT University News Services

CHICAGO, March 11, 2021 /PRNewswire/ -- According to the new market research report "Artificial Intelligence In Genomics Market by Offering (Software, Services),Technology (Machine Learning, Computer Vision), Functionality (Genome Sequencing, Gene Editing), Application (Diagnostics), End User (Pharma, Research) - Global Forecasts to 2025", published by MarketsandMarkets, the global AI in Genomics market is projected to reach USD 1,671 million by 2025 from USD 202 million in 2020, at a CAGR of 52.7% between 2020 and 2025

Browse in-depth TOC on "Artificial Intelligence in Genomics Market"141 Tables24 Figures 154 Pages

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The need to control drug development and discovery costs and time, increasing public and private investments in AI in genomics, and the adoption of AI solutions in precision medicine are driving the growth of this market. However, the lack of a skilled AI workforce and ambiguous regulatory guidelines for medical software are expected to restrain the market growth during the forecast period.

Machine learning to dominate the AI in Genomics market in 2019

Based on technology, the Artificial Intelligence in GenomicsMarket is segmented into machine learning and other technologies. The machine learning segment dominated this market in 2019, as pharmaceutical companies, CROs, and biotechnology companies have widely adopted machine learning for drug genomics applications. This is because machine learning can extract insights from data sets, accelerating genomic research.

Diagnostics segment accounted for the largest share of the AI in Genomics market, by end user, in 2019

Based on application, the Artificial Intelligence in GenomicsMarket is segmented into diagnostics, drug discovery & development, precision medicine, agriculture & animal research, and other applications. Diagnostics was the largest application segment in genomics market in 2019. The large share of this segment can be attributed to the increasing research on diseases and the decreasing cost of sequencing.

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North America is the largest regional market for AI in Genomics in 2019

In 2019, North America accounted for the largest share of the AI in Genomics market, followed by Europe. The large share of North America can be attributed to the increasing research funding and government initiatives for promoting precision medicine in the US.

Prominent players in the Artificial Intelligence in GenomicsMarket are IBM (US), Microsoft (US), NVIDIA Corporation (US), Deep Genomics (Canada), BenevolentAI (UK), Fabric Genomics Inc. (US), Verge Genomics (US), Freenome Holdings, Inc. (US), MolecularMatch Inc. (US), Cambridge Cancer Genomics (UK), SOPHiA GENETICS (US), Data4Cure Inc. (US), PrecisionLife Ltd (UK),Genoox Ltd. (US), Lifebit (UK), Diploid (Belgium), FDNA Inc. (US), DNAnexus Inc. (US), Empiric Logic (Ireland), Engine Biosciences Pte. Ltd. (US)

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Artificial Intelligence (AI) in Drug Discovery Market by Component (Software, Service), Technology (ML, DL), Application (Neurodegenerative Diseases, Immuno-Oncology, CVD), End User (Pharmaceutical & Biotechnology, CRO), Region - Global forecast to 2024https://www.marketsandmarkets.com/Market-Reports/ai-in-drug-discovery-market-151193446.html

Genomics Market by Product & Service (System & Software, Consumables, Services), Technology (Sequencing, PCR), Application (Drug Discovery & Development, Diagnostic, Agriculture), End User (Hospital & Clinics, Research Centers) Global Forecast to 2025https://www.marketsandmarkets.com/Market-Reports/genomics-market-613.html

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Artificial Intelligence in Genomics Market worth $1,671 million by 2025 - Exclusive Report by MarketsandMarkets - PRNewswire

Software that uses artificial intelligence (AI) may help improve breast cancer diagnosis.

QuantX, developed in Chicago, uses AI to analyze breast MRIs. Radiologists can use the technology to help assess if breast lesions are cancerous. Research shows the technology led to a 39% reduction in missed cancers, according to a clinical trial.

Maryellen Giger, PhD, a professor of radiology at the University of Chicago, developed the technology, which the FDA cleared in 2017. You can think of breast cancer screening as Wheres Waldo? she says, referring to the puzzle books where one searches for a character who blends in with background images.

QuantX, now owned by Chicago-based company Qlarity Imaging, generates a 3-D image that radiologists can rotate to see the size and location of a tumor. They can use that image to decide whether to conduct a biopsy.

Though patients are unlikely to know if a doctor used the software, its now in hospitals and imaging centers around the country. Down the line, similar software could be used to diagnose other cancers, like in the prostate and lung.

Susan Cosier is a Chicago-based writer focused on science and the environment. Her work has appeared in Scientific American and Science.

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Using Artificial Intelligence to Assess Breast Cancer - Chicago Health