Archive for category Artificial intelligence

[WEB PAGE] Playing video games helps stroke recovery

by Hayley Jarvis, Brunel University

Playing video games helps stroke recovery
Credit: Neurofenix

A genius game controller helping stroke patients get back hand and arm movement by playing on the computer is set to start tests in a stroke unit.

1.5 million Brits have a stroke and 70 percent of them get weakness in their hands and arms, leaving many unable to even make a cup of tea or get dressed.

The NeuroBall is shown to help people regain strength and movement in their arms and hands after a stroke by making dull daily rehab exercises more fun.

Now makers Brunel University London and UK firm Neurofenix have won £60,000 from The Stoke Association and MedCity to take development to the next level.

“Neurofenix and Brunel University London will explore how this new hand-held console and app could improve the recovery of hand and arm movement at vital, earlier stages of recovery,” said Dr. Richard Francis, Head of Research Awards at the Stroke Association.

“They will also look at whether the new technology can be cost-effective for the NHS.”

The only route to recovery is through rehab and key to that is practice. It can take months to get back enough movement and control to do everyday tasks, said Brunel’s Dr. Cherry Kilbride: “Doing repetitive exercise is boring and engaging the person in enough practice to make a difference is a challenge given NHS resources.”

Stroke survivors can play nine themed video games holding the Neuroball console, which uses AI to track arm and hand movements and send feedback to an app. Developed by the team at Brunel along with Neurofenix and stroke survivors, it’s designed to motivate users to do hundreds of physio reps in the comfort of their home without even realizing it.

“Our vision is to enable stroke survivors and patients suffering neurological conditions to regain their independence. Brunel University London helped us validate how our technology can aid stroke survivors,” said Neurofenix’s Dimitris Athanasiou.

Playing video games helps stroke recovery
Credit: Neurofenix

Hospital stroke rehab patients move their arm and hand an average 32 times in a session. But survivors who played videogames with the Neuroball practiced an average 17 hours a week, notching up 15,000 reps over seven weeks, an earlier study showed.

“These latest findings demonstrate the need and importance of stroke research,” said Dr. Francis. “However, the pandemic has heavily impacted research funding and it’s vital that breakthroughs like this continue to be funded, so that stroke survivors get the support they need to rebuild their lives.”

Playing video games helps stroke recovery
Credit: Neurofenix

Starting in January, the new study will track stroke patients who are in an earlier stage of recovery, first at Hillingdon stroke unit and then their first weeks at home.

“We know from an earlier study that the NeuroBall and appis safe, feasible and acceptable to stroke survivors living at home more than sixmonths post stroke,” said Dr. Kilbride. “But we do not yet know about using the intervention in the earlier stroke pathway.”

Explore further Keys to the best possible stroke recovery even now

More information: The new study is called RHOMBUS II: Rehabilitation using Virtual Gaming for Hospital and HOMe-Based Upper-limb exercise post Stroke (RHOMBUS): a feasibility randomised controlled trial.

Provided by Brunel University


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[WEB PAGE] Researchers develop an AI system with near-perfect seizure prediction

It’s 99.6 accurate detecting seizures up to an hour before they happen.

mr.suphachai praserdumrongchai via Getty Images

We’ve seen a smart arm bracelet that can predict nightly seizures, but now a pair of researchers have created something even more promising: an AI system that can predict epileptic seizures with 99.6-percent accuracy. Even better, it can do so up to an hour before they occur. As IEEE Spectrum reports, that gives people enough time to prepare for the attack by taking medication. Around 50 million people around the world currently have epilepsy, according to the World Health Organization, and 70 percent of those patients can control their seizures with medication.

While it’s not a complete fix, the new AI system, developed by Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette, is a major leap forward from existing prediction methods. Currently, other methods analyze brain activity with an EEG (electroencephalogram) test and apply a predictive model afterwards. The new method does both of those things at once, with the help of a deep learning algorithm that maps brain activity and another that can predict the electrical channels lighting up during a seizure.

It’ll still be some time before this technique will be available for widespread use — the team is now working on a custom chip that can help process the necessary algorithms — but it could be life-changing news for patients with epilepsy.


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[NEWS] B-Temia gains traction with 510(k) clearance for mobility device

Man wearing Keeogo about to climb stairs
Keeogo provides stability and strength to stroke patients with limited mobility. Credit: B-Temia Inc.

September 14, 2020 By Annette Boyle

B-Temia Inc.’s Keeogo mobility device is on the move in the U.S. now that it has received 510(k) clearance from the U.S. FDA. Unlike currently available exoskeletons that move for patients, the Keeogo (keep on going) Dermoskeleton system amplifies signals from patients who can initiate movement but need additional assistance.

“This U.S. market clearance is the biggest milestone of our global regulatory expansion, as the USA is the largest medical device market. It also gives us great confidence for the other regulatory approvals we are currently completing for additional territories,” said B-Temia’s president and CEO Stéphane Bédard.

The U.S. action specifically covers use of the device for stroke patients in rehabilitation settings. “Stroke is just the entry door,” Bédard told BioWorld. “We want to extend U.S. authorization for other indications in the future. We’ve done very well for stroke patients and want to do the same for those with multiple sclerosis, osteoarthritis of the knee, Parkinson’s disease, and partial spinal cord injuries.”

The company also hopes to gain clearance for patients to use the device on a day-to-day basis, not just during rehab sessions. “Keeogo has as its main purpose providing the person the ability to regain their activity on a daily basis walking, shopping, out in the yard. That’s why we invented it,” Bédard added. “We will reach that level in the U.S., but with the FDA, you have to go step-by-step for each indication.”

Keeogo already has much broader authorization in Europe where it received CE mark authorization in December 2019. In the 28 European countries covered by the CE mark, Quebec-based B-Temia can market the system to provide additional strength and stability to users with musculoskeletal weakness or lower limb instability both at home and in clinics. The system has been approved by Health Canada since 2015 for a range of indications as well.

The technology

Keeogo is a lightweight motorized walking assistive device that boosts leg power. Its dermoskeleton technology employs artificial intelligence (AI) to help individuals with impaired mobility walk, run, sit, and climb. Underpinned by a model of human biomechanics and the basics elements of gait, the AI uses additional mathematical equations to intervene properly in the movement.

The AI, housed on a belt worn at the waist, interprets information transmitted by sensors strapped to the leg to understand the user’s intent and then provides the compensation needed so they can achieve their goal. It is unique in that it does not replace an individual’s motion, only augments it. “If you don’t walk, it won’t move,” said Bédard. “It will add its response to your own characteristic speed and cadence and is fully customizable to the specifics of a disease and person. We’re only able to achieve this level of sophistication with AI.”

By augmenting the user’s motions, Keeogo works to help them regain or retain their autonomy and mobility. “When you go in the lab with Keeogo, you extend your range of motion, augment stride length, and increase the biomechanical ability to walk,” explained Bédard. “When you repeat recursive exercises, you build your capacity. You extend what you’ve done in the past– the body has a memory of that – and Keeogo synchronizes the motions, extends the gait, so that day after day you regain capacity.” In Parkinson’s and other degenerative diseases, the system helps patients hold onto their independence and not fall into a pattern of doing less and less as the disease progresses and movement becomes more challenging.

Notably, the system is not tied to an idealized motion. “We’re not trying to perfect the individual’s gait, just to improve it. We want to keep the individual’s natural gait. They will improve themselves as they use the system,” Bédard said.

Aside from its clinical applications, B-Temia also continues to develop its military version of Keeogo, the Onyx exoskeleton, for the U.S. Army. It has worked with Lockheed Martin since 2017 to support soldiers tasked with carrying loads of more than 100 pounds. Under that weight, people naturally change their gait. In addition, the weight puts such pressure on the joints that it often leads to both acute and chronic musculoskeletal injuries.

Future plans

“The approval also confers additional credibility for the corporation that will open a lot of doors in terms of investors, financing, and partnerships,” Bédard said.

He plans to spend the next several weeks determining how to execute properly on commercialization in the U.S. and elsewhere so that the device can be easily acquired by individuals who could benefit. “Our next challenge is to establish a good strategy. There are many options on the table and we want to make sure we choose the right structure, partners and channels.


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[WEB SITE] New method based on artificial intelligence may help predict epilepsy outcomes


Medical University of South Carolina (MUSC) neurologists have developed a new method based on artificial intelligence that may eventually help both patients and doctors weigh the pros and cons of using brain surgery to treat debilitating seizures caused by epilepsy. This study, which focused on mesial temporal lobe epilepsy (TLE), was published in the September 2018 issue of Epilepsia. Beyond the clinical implications of incorporating this analytical method into clinicians’ decision making processes, this work also highlights how artificial intelligence is driving change in the medical field.

Despite the increase in the number of epilepsy medications available, as many as one-third of patients are refractory, or non-responders, to the medication. Uncontrolled epilepsy has many dangers associated with seizures, including injury from falls, breathing problems, and even sudden death. Debilitating seizures from epilepsy also greatly reduce quality of life, as normal activities are impaired.

Epilepsy surgery is often recommended to patients who do not respond to medications. Many patients are hesitant to undergo brain surgery, in part, due to fear of operative risks and the fact that only about two-thirds of patients are seizure-free one year after surgery. To tackle this critical gap in the treatment of this epilepsy population, Dr. Leonardo Bonilha and his team in the Department of Neurology at MUSC looked to predict which patients are likely to have success in being seizure free after the surgery.

Neurology Department Chief Resident Dr. Gleichgerrcht explains that they tried “to incorporate advanced neuroimaging and computational techniques to anticipate surgical outcomes in treating seizures that occur with loss of consciousness in order to eventually enhance quality of life”. In order to do this, the team turned to a computational technique, called deep learning, due to the massive amount of data analysis required for this project.

The whole-brain connectome, the key component of this study, is a map of all physical connections in a person’s brain. The brain map is created by in-depth analysis of diffusion magnetic resonance imaging (dMRI), which patients receive as standard-of-care in the clinic. The brains of epilepsy patients were imaged by dMRI prior to having surgery.

Deep learning is a statistical computational approach, within the realm of artificial intelligence, where patterns in data are automatically learned. The physical connections in the brain are very individualized and thus it is challenging to find patterns across multiple patients. Fortunately, the deep learning method is able to isolate the patterns in a more statistically reliable method in order to provide a highly accurate prediction.

Currently, the decision to perform brain surgery on a refractory epilepsy patient is made based on a set of clinical variables including visual interpretation of radiologic studies. Unfortunately, the current classification model is 50 to 70 percent accurate in predicting patient outcomes post-surgery. The deep learning method that the MUSC neurologists developed was 79 to 88 percent accurate. This gives the doctors a more reliable tool for deciding whether the benefits of surgery outweigh the risks for the patient.

A further benefit of this new technique is that no extra diagnostic tests are required for the patients, since dMRIs are routinely performed with epilepsy patients at most centers.

This first study was retrospective in nature, meaning that the clinicians looked at past data. The researchers propose that an ideal next step would include a multi-site prospective study. In a prospective study, they would analyze the dMRI scans of patients prior to surgery and follow-up with the patients for at least one year after surgery. The MUSC neurologists also believe that integrating the brain’s functional connectome, which is a map of simultaneously occurring neural activity across different brain regions, could enhance the prediction of outcomes.

Dr. Gleichgerrcht says that the novelty in the development of this study lies in the fact that this “is not a question of human versus machine, as is often the fear when we hear about artificial intelligence. In this case, we are using artificial intelligence as an extra tool to eventually make better informed decisions regarding a surgical intervention that holds the hope for a cure of epilepsy in a large number of patients.”


via New method based on artificial intelligence may help predict epilepsy outcomes

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