COVID Variants Are Like “a Thief Changing Clothes” – and Our Camera System Barely Exists
Whether it's "natural selection" as Darwin called it, or it's "mutating" as the X-Men called it, living organisms change over time, developing thumbs or more efficient protein spikes, depending on the organism and the demands of its environment. The coronavirus that causes COVID-19, SARS-CoV-2, is not an exception, and now, after the virus has infected millions of people around the globe for more than a year, scientists are beginning to see those changes.
The notorious variants that have popped up include B.1.1.7, sometimes called the UK variant, as well as P.1 and B.1.351, which seem to have emerged in Brazil and South Africa respectively. As vaccinations are picking up pace, officials are warning that now
is not the time to become complacent or relax restrictions because the variants aren't well understood.
Some appear to be more transmissible, and deadlier, while others can evade the immune system's defenses better than earlier versions of the virus, potentially undermining the effectiveness of vaccines to some degree. Genomic surveillance, the process of sequencing the genetic code of the virus widely to observe changes and patterns, is a critical way that scientists can keep track of its evolution and work to understand how the variants might affect humans.
"It's like a thief changing clothes"
It's important to note that viruses mutate all the time. If there were funding and personnel to sequence the genome of every sample of the virus, scientists would see thousands of mutations. Not every variant deserves our attention. The vast majority of mutations are not important at all, but recognizing those that are is a crucial tool in getting and staying ahead of the virus. The work of sequencing, analyzing, observing patterns, and using public health tools as necessary is complicated and confusing to those without years of specialized training.
Jeremy Kamil, associate professor of microbiology and immunology at LSU Health Shreveport, in Louisiana, says that the variants developing are like a thief changing clothes. The thief goes in your house, steals your stuff, then leaves and puts on a different shirt and a wig, in the hopes you won't recognize them. Genomic surveillance catches the "thief" even in those different clothes.
One of the tricky things about variants is recognizing the point at which they move from interesting, to concerning at a local level, to dangerous in a larger context.
Understanding variants, both the uninteresting ones and the potentially concerning ones, gives public health officials and researchers at different levels a useful set of tools. Locally, knowing which variants are circulating in the community helps leaders know whether mask mandates and similar measures should be implemented or discontinued, or whether businesses and schools can open relatively safely.
There's more to it than observing new variants
Analysis is complex, particularly when it comes to understanding which variants are of concern. "So the question is always if a mutation becomes common, is that a random occurrence?" says Phoebe Lostroh, associate professor of molecular biology at Colorado College. "Or is the variant the result of some kind of selection because the mutation changes some property about the virus that makes it reproduce more quickly than variants of the virus that don't have that mutation? For a virus, [mutations can affect outcomes like] how much it replicates inside a person's body, how much somebody breathes it out, whether the particles that somebody might breathe in get smaller and can lead to greater transmission."
Along with all of those factors, accurate and useful genomic surveillance requires an understanding of where variants are occurring, how they are related, and an examination of why they might be prevalent.
For example, if a potentially worrisome variant appears in a community and begins to spread very quickly, it's not time to raise a public health alarm until several important questions have been answered, such as whether the variant is spreading due to specific events, or if it's happening because the mutation has allowed the virus to infect people more efficiently. Kamil offered a hypothetical scenario to explain: Imagine that a member of a community became infected and the virus mutated. That person went to church and three more people were infected, but one of them went to a karaoke bar and while singing infected 100 other people. Examining the conditions under which the virus has spread is, therefore, an essential part of untangling whether a mutation itself made the virus more transmissible or if an infected person's behaviors contributed to a local outbreak.
One of the tricky things about variants is recognizing the point at which they move from interesting, to concerning at a local level, to dangerous in a larger context. Genomic sequencing can help with that, but only when it's coordinated. When the same mutation occurs frequently, but is localized to one region, it's a concern, but when the same mutation happens in different places at the same time, it's much more likely that the "virus is learning that's a good mutation," explains Kamil.
The process is called convergent evolution, and it was a fascinating topic long before COVID. Just as your heritage can be traced through DNA, so can that of viruses, and when separate lineages develop similar traits it's almost like scientists can see evolution happening in real time. A mutation to SARS-CoV-2 that happens in more than one place at once is a mutation that makes it easier in some way for the virus to survive and that is when it may become alarming. The widespread, documented variants P.1 and B.1.351 are examples of convergence because they share some of the same virulent mutations despite having developed thousands of miles apart.
However, even variants that are emerging in different places at the same time don't present the kind of threat SARS-CoV-2 did in 2019. "This is nature," says Kamil. "It just means that this virus will not easily be driven to extinction or complete elimination by vaccines." Although a person who has already had COVID-19 can be reinfected with a variant, "it is almost always much milder disease" than the original infection, Kamil adds. Rather than causing full-fledged disease, variants have the potiental to "penetrate herd immunity, spreading relatively quietly among people who have developed natural immunity or been vaccinated, until the virus finds someone who has no immunity yet, and that person would be at risk of hospitalization-grade severe disease or death."
Surveillance and predictions
According to Lostroh, genomic surveillance can help scientists predict what's going to happen. "With the British strain, for instance, that's more transmissible, you can measure how fast it's doubling in the population and you can sort of tell whether we should take more measures against this mutation. Should we shut things down a little longer because that mutation is present in the population? That could be really useful if you did enough sampling in the population that you knew where it was," says Lostroh. If, for example, the more transmissible strain was present in 50 percent of cases, but in another county or state it was barely present, it would allow for rolling lockdowns instead of sweeping measures.
Variants are also extremely important when it comes to the development, manufacture, and distribution of vaccines. "You're also looking at medical countermeasures, such as whether your vaccine is still effective, or if your antiviral needs to be updated," says Lane Warmbrod, a senior analyst and research associate at Johns Hopkins Center for Health Security.
Properly funded and extensive genomic surveillance could eventually help control endemic diseases, too, like the seasonal flu, or other common respiratory infections. Kamil says he envisions a future in which genomic surveillance allows for prediction of sickness just as the weather is predicted today. "It's a 51 for infection today at the San Francisco Airport. There's been detection of some respiratory viruses," he says, offering an example. He says that if you're a vulnerable person, if you're immune-suppressed for some reason, you may want to wear a mask based on the sickness report.
The U.S. has the ability, but lacks standards
The benefits of widespread genomic surveillance are clear, and the United States certainly has the necessary technology, equipment, and personnel to carry it out. But, it's not happening at the speed and extent it needs to for the country to gain the benefits.
"The numbers are improving," said Kamil. "We're probably still at less than half a percent of all the samples that have been taken have been sequenced since the beginning of the pandemic."
Although there's no consensus on how many sequences is ideal for a robust surveillance program, modeling performed by the company Illumina suggests about 5 percent of positive tests should be sequenced. The reasons the U.S. has lagged in implementing a sequencing program are complex and varied, but solvable.
Perhaps the most important element that is currently missing is leadership. In order to conduct an effective genomic surveillance program, there need to be standards. The Johns Hopkins Center for Health Security recently published a paper with recommendations as to what kinds of elements need to be standardized in order to make the best use of sequencing technology and analysis.
"Along with which bioinformatic pipelines you're going to use to do the analyses, which sequencing strategy protocol are you going to use, what's your sampling strategy going to be, how is the data is going to be reported, what data gets reported," says Warmbrod. Currently, there's no guidance from the CDC on any of those things. So, while scientists can collect and report information, they may be collecting and reporting different information that isn't comparable, making it less useful for public health measures and vaccine updates.
Globally, one of the most important tools in making the information from genomic surveillance useful is GISAID, a platform designed for scientists to share -- and, importantly, to be credited for -- their data regarding genetic sequences of influenza. Originally, it was launched as a database of bird flu sequences, but has evolved to become an essential tool used by the WHO to make flu vaccine virus recommendations each year. Scientists who share their credentials have free access to the database, and anyone who uses information from the database must credit the scientist who uploaded that information.
Safety, logistics, and funding matter
Scientists at university labs and other small organizations have been uploading sequences to GISAID almost from the beginning of the pandemic, but their funding is generally limited, and there are no standards regarding information collection or reporting. Private, for-profit labs haven't had motivation to set up sequencing programs, although many of them have the logistical capabilities and funding to do so. Public health departments are understaffed, underfunded, and overwhelmed.
University labs may also be limited by safety concerns. The SARS-CoV-2 virus is dangerous, and there's a question of how samples should be transported to labs for sequencing.
Larger, for-profit organizations often have the tools and distribution capabilities to safely collect and sequence samples, but there hasn't been a profit motive. Genomic sequencing is less expensive now than ever before, but even at $100 per sample, the cost adds up -- not to mention the cost of employing a scientist with the proper credentials to analyze the sequence.
The path forward
The recently passed COVID-19 relief bill does have some funding to address genomic sequencing. Specifically, the American Rescue Plan Act includes $1.75 billion in funding for the Centers for Disease Control and Prevention's Advanced Molecular Detection (AMD) program. In an interview last month, CDC Director Rochelle Walensky said that the additional funding will be "a dial. And we're going to need to dial it up." AMD has already announced a collaboration called the Sequencing for Public Health Emergency Response, Epidemiology, and Surveillance (SPHERES) Initiative that will bring together scientists from public health, academic, clinical, and non-profit laboratories across the country with the goal of accelerating sequencing.
Such a collaboration is a step toward following the recommendations in the paper Warmbrod coauthored. Building capacity now, creating a network of labs, and standardizing procedures will mean improved health in the future. "I want to be optimistic," she says. "The good news is there are a lot of passionate, smart, capable people who are continuing to work with government and work with different stakeholders." She cautions, however, that without a national strategy we won't succeed.
"If we maximize the potential and create that framework now, we can also use it for endemic diseases," she says. "It's a very helpful system for more than COVID if we're smart in how we plan it."
Send in the Robots: A Look into the Future of Firefighting
April in Paris stood still. Flames engulfed the beloved Notre Dame Cathedral as the world watched, horrified, in 2019. The worst looked inevitable when firefighters were forced to retreat from the out-of-control fire.
But the Paris Fire Brigade had an ace up their sleeve: Colossus, a firefighting robot. The seemingly indestructible tank-like machine ripped through the blaze with its motorized water cannon. It was able to put out flames in places that would have been deadly for firefighters.
Firefighting is entering a new era, driven by necessity. Conventional methods of managing fires have been no match for the fiercer, more expansive fires being triggered by climate change, urban sprawl, and susceptible wooded areas.
Robots have been a game-changer. Inspired by Paris, the Los Angeles Fire Department (LAFD) was the first in the U.S. to deploy a firefighting robot in 2021, the Thermite Robotics System 3 – RS3, for short.
RS3 is a 3,500-pound turbine on a crawler—the size of a Smart car—with a 36.8 horsepower engine that can go for 20 hours without refueling. It can plow through hazardous terrain, move cars from its path, and pull an 8,000-pound object from a fire.
All that while spurting 2,500 gallons of water per minute with a rear exhaust fan clearing the smoke. At a recent trade show, RS3 was billed as equivalent to 10 firefighters. The Los Angeles Times referred to it as “a droid on steroids.”
Robots such as the Thermite RS3 can plow through hazardous terrain and pull an 8,000-pound object from a fire.
Los Angeles Fire Department
The advantage of the robot is obvious. Operated remotely from a distance, it greatly reduces an emergency responder’s exposure to danger, says Wade White, assistant chief of the LAFD. The robot can be sent into airplane fires, nuclear reactors, hazardous areas with carcinogens (think East Palestine, Ohio), or buildings where a roof collapse is imminent.
Advances for firefighters are taking many other forms as well. Fibers have been developed that make the firefighter’s coat lighter and more protective from carcinogens. New wearable devices track firefighters’ biometrics in real time so commanders can monitor their heat stress and exertion levels. A sensor patch is in development which takes readings every four seconds to detect dangerous gases such as methane and carbon dioxide. A sonic fire extinguisher is being explored that uses low frequency soundwaves to remove oxygen from air molecules without unhealthy chemical compounds.
The demand for this technology is only increasing, especially with the recent rise in wildfires. In 2021, fires were responsible for 3,800 deaths and 14,700 injuries of civilians in this country. Last year, 68,988 wildfires burned down 7.6 million acres. Whether the next generation of firefighting can address these new challenges could depend on special cameras, robots of the aerial variety, AI and smart systems.
Fighting fire with cameras
Another key innovation for firefighters is a thermal imaging camera (TIC) that improves visibility through smoke. “At a fire, you might not see your hand in front of your face,” says White. “Using the TIC screen, you can find the door to get out safely or see a victim in the corner.” Since these cameras were introduced in the 1990s, the price has come down enough (from $10,000 or more to about $700) that every LAFD firefighter on duty has been carrying one since 2019, says White.
TICs are about the size of a cell phone. The camera can sense movement and body heat so it is ideal as a search tool for people trapped in buildings. If a firefighter has not moved in 30 seconds, the motion detector picks that up, too, and broadcasts a distress signal and directional information to others.
To enable firefighters to operate the camera hands-free, the newest TICs can attach inside a helmet. The firefighter sees the images inside their mask.
TICs also can be mounted on drones to get a bird’s-eye, 360 degree view of a disaster or scout for hot spots through the smoke. In addition, the camera can take photos to aid arson investigations or help determine the cause of a fire.
More help From above
Firefighters prefer the term “unmanned aerial systems” (UAS) to drones to differentiate them from military use.
A UAS carrying a camera can provide aerial scene monitoring and topography maps to help fire captains deploy resources more efficiently. At night, floodlights from the drone can illuminate the landscape for firefighters. They can drop off payloads of blankets, parachutes, life preservers or radio devices for stranded people to communicate, too. And like the robot, the UAS reduces risks for ground crews and helicopter pilots by limiting their contact with toxic fumes, hazardous chemicals, and explosive materials.
“The nice thing about drones is that they perform multiple missions at once,” says Sean Triplett, team lead of fire and aviation management, tools and technology at the Forest Service.
Experts predict we’ll see swarms of drones dropping water and fire retardant on burning buildings and forests in the near future.
The UAS is especially helpful during wildfires because it can track fires, get ahead of wind currents and warn firefighters of wind shifts in real time. The U.S. Forest Service also uses long endurance, solar-powered drones that can fly for up to 30 days at a time to detect early signs of wildfire. Wildfires are no longer seasonal in California – they are a year-long threat, notes Thanh Nguyen, fire captain at the Orange County Fire Authority.
In March, Nguyen’s crew deployed a drone to scope out a huge landslide following torrential rains in San Clemente, CA. Emergency responders used photos and videos from the drone to survey the evacuated area, enabling them to stay clear of ground on the hillside that was still sliding.
Improvements in drone batteries are enabling them to fly for longer with heavier payloads. Experts predict we’ll see swarms of drones dropping water and fire retardant on burning buildings and forests in the near future.
AI to the rescue
The biggest peril for a firefighter is often what they don’t see coming. Flashovers are a leading cause of firefighter deaths, for example. They occur when flammable materials in an enclosed area ignite almost instantaneously. Or dangerous backdrafts can happen when a firefighter opens a window or door; the air rushing in can ignite a fire without warning.
The Fire Fighting Technology Group at the National Institute of Standards and Technology (NIST) is developing tools and systems to predict these potentially lethal events with computer models and artificial intelligence.
Partnering with other institutions, NIST researchers developed the Flashover Prediction Neural Network (FlashNet) after looking at common house layouts and running sets of scenarios through a machine-learning model. In the lab, FlashNet was able to predict a flashover 30 seconds before it happened with 92.1% success. When ready for release, the technology will be bundled with sensors that are already installed in buildings, says Anthony Putorti, leader of the NIST group.
The NIST team also examined data from hundreds of backdrafts as a basis for a machine-learning model to predict them. In testing chambers the model predicted them correctly 70.8% of the time; accuracy increased to 82.4% when measures of backdrafts were taken in more positions at different heights in the chambers. Developers are working on how to integrate the AI into a small handheld device that can probe the air of a room through cracks around a door or through a created opening, Putorti says. This way, the air can be analyzed with the device to alert firefighters of any significant backdraft risk.
Early wildfire detection technologies based on AI are in the works, too. The Forest Service predicts the acreage burned each year during wildfires will more than triple in the next 80 years. By gathering information on historic fires, weather patterns, and topography, says White, AI can help firefighters manage wildfires before they grow out of control and create effective evacuation plans based on population data and fire patterns.
The future is connectivity
We are in our infancy with “smart firefighting,” says Casey Grant, executive director emeritus of the Fire Protection Research Foundation. Grant foresees a new era of cyber-physical systems for firefighters—a massive integration of wireless networks, advanced sensors, 3D simulations, and cloud services. To enhance teamwork, the system will connect all branches of emergency responders—fire, emergency medical services, law enforcement.
FirstNet (First Responder Network Authority) now provides a nationwide high-speed broadband network with 5G capabilities for first responders through a terrestrial cell network. Battling wildfires, however, the Forest Service needed an alternative because they don’t always have access to a power source. In 2022, they contracted with Aerostar for a high altitude balloon (60,000 feet up) that can extend cell phone power and LTE. “It puts a bubble of connectivity over the fire to hook in the internet,” Triplett explains.
A high altitude balloon, 60,000 feet high, can extend cell phone power and LTE, putting a "bubble" of internet connectivity over fires.
Courtesy of USDA Forest Service
Advances in harvesting, processing and delivering data will improve safety and decision-making for firefighters, Grant sums up. Smart systems may eventually calculate fire flow paths and make recommendations about the best ways to navigate specific fire conditions. NIST’s plan to combine FlashNet with sensors is one example.
The biggest challenge is developing firefighting technology that can work across multiple channels—federal, state, local and tribal systems as well as for fire, police and other emergency services— in any location, says Triplett. “When there’s a wildfire, there are no political boundaries,” he says. “All hands are on deck.”
New device can diagnose concussions using AI
For a long time after Mary Smith hit her head, she was not able to function. Test after test came back normal, so her doctors ruled out the concussion, but she knew something was wrong. Finally, when she took a test with a novel EyeBOX device, recently approved by the FDA, she learned she indeed had been dealing with the aftermath of a concussion.
“I felt like even my husband and doctors thought I was faking it or crazy,” recalls Smith, who preferred not to disclose her real name. “When I took the EyeBOX test it showed that my eyes were not moving together and my BOX score was abnormal.” To her diagnosticians, scientists at the Minneapolis-based company Oculogica who developed the EyeBOX, these markers were concussion signs. “I cried knowing that finally someone could figure out what was wrong with me and help me get better,” she says.
Concussion affects around 42 million people worldwide. While it’s increasingly common in the news because of sports injuries, anything that causes damage to the head, from a fall to a car accident, can result in a concussion. The sudden blow or jolt can disrupt the normal way the brain works. In the immediate aftermath, people may suffer from headaches, lose consciousness and experience dizziness, confusion and vomiting. Some recover but others have side effects that can last for years, particularly affecting memory and concentration.
There is no simple standard-of-care test to confirm a concussion or rule it out. Neither do they appear on MRI and CT scans. Instead, medical professionals use more indirect approaches that test symptoms of concussions, such as assessments of patients’ learning and memory skills, ability to concentrate and problem solving. They also look at balance and coordination. Most tests are in the form of questionnaires or symptom checklists. Consequently, they have limitations, can be biased and may miss a concussion or produce a false positive. Some people suspected of having a concussion may ordinarily have difficulties with literary and problem-solving tests because of language challenges or education levels.
Another problem with current tests is that patients, particularly soldiers who want to return to combat and athletes who would like to keep competing, could try and hide their symptoms to avoid being diagnosed with a brain injury. Trauma physicians who work with concussion patients have the need for a tool that is more objective and consistent.
“This type of assessment doesn’t rely on the patient's education level, willingness to follow instructions or cooperation. You can’t game this.” -- Uzma Samadani, founder of Oculogica
“The importance of having an objective measurement tool for the diagnosis of concussion is of great importance,” says Douglas Powell, associate professor of biomechanics at the University of Memphis, with research interests in sports injury and concussion. “While there are a number of promising systems or metrics, we have yet to develop a system that is portable, accessible and objective for use on the sideline and in the clinic. The EyeBOX may be able to address these issues, though time will be the ultimate test of performance.”
The EyeBOX as a window inside the brain
Using eye movements to diagnose a concussion has emerged as a promising technique since around 2010. Oculogica combined eye movements with AI to develop the EyeBOX to develop an unbiased objective diagnostic tool.
“What’s so great about this type of assessment is it doesn’t rely on the patient's education level, willingness to follow instructions or cooperation,” says Uzma Samadani, a neurosurgeon and brain injury researcher at the University of Minnesota, who founded Oculogica. “You can’t game this. It assesses functions that are prompted by your brain.”
In 2010, Samadani was working on a clinical trial to improve the outcome of brain injuries. The team needed some way to measure if seriously brain injured patients were improving. One thing patients could do was watch TV. So Samadani designed and patented an AI-based algorithm that tracks the relationship between eye movement and concussion.
The EyeBOX test requires patients to watch movie or music clips for 220 seconds. An eye tracking camera records subconscious eye movements, tracking eye positions 500 times per seconds as patients watch the video. It collects over 100,000 data points. The device then uses AI to assess whether there’s any disruptions from the normal way the eyes move.
Cranial nerves are responsible for transmitting information between the brain and the body. Many are involved in eye movement. Pressure caused by a concussion can affect how these nerves work. So tracking how the eyes move can indicate if there’s anything wrong with the cranial nerves and where the problem lies.
If someone is healthy, their eyes should be able to focus on an object, follow movement and both eyes should be coordinated with each other. The EyeBox can detect abnormalities. For example, if a patient’s eyes are coordinated but they are not moving as they should, that indicates issues in the central brain stem, whilst only one eye moving abnormally suggests that a particular nerve section is affected.
Uzma Samadani with the EyeBOX device
Courtesy Oculogica
“The EyeBOX is a monitor for cranial nerves,” says Samadani. “Essentially it’s a form of digital neurological exam. “Several other eye-tracking techniques already exist, but they rely on subjective self-reported symptoms. Many also require a baseline, a measure of how patients reacted when they were healthy, which often isn’t available.
VOMS (Vestibular Ocular Motor Screen) is one of the most accurate diagnostic tests used in clinics in combination with other tests, but it is subjective. It involves a therapist getting patients to move their head or eyes as they focus or follow a particular object. Patients then report their symptoms.
The King-Devick test measures how fast patients can read numbers and compares it to a baseline. Since it is mainly used for athletes, the initial test is completed before the season starts. But participants can manipulate it. It also cannot be used in emergency rooms because the majority of patients wouldn’t have prior baseline tests.
Unlike these tests, EyeBOX doesn’t use a baseline and is objective because it doesn’t rely on patients’ answers. “It shows great promise,” says Thomas Wilcockson, a senior lecturer of psychology in Loughborough University, who is an expert in using eye tracking techniques in neurological disorders. “Baseline testing of eye movements is not always possible. Alternative measures of concussion currently in development, including work with VR headsets, seem to currently require it. Therefore the EyeBOX may have an advantage.”
A technology that’s still evolving
In their last clinical trial, Oculogica used the EyeBOX to test 46 patients who had concussion and 236 patients who did not. The sensitivity of the EyeBOX, or the probability of it correctly identifying the patient’s concussion, was 80.4 percent. Meanwhile, the test accurately ruled out a concussion in 66.1 percent of cases. This is known as its specificity score.
While the team is working on improving the numbers, experts who treat concussion patients find the device promising. “I strongly support their use of eye tracking for diagnostic decision making,” says Douglas Powell. “But for diagnostic tests, we would prefer at least one of the sensitivity or specificity values to be greater than 90 percent. Powell compares EyeBOX with the Buffalo Concussion Treadmill Test, which has sensitivity and specificity values of 73 and 78 percent, respectively. The VOMS also has shown greater accuracy than the EyeBOX, at least for now. Still, EyeBOX is competitive with the best diagnostic testing available for concussion and Powell hopes that its detection prowess will improve. “I anticipate that the algorithms being used by Oculogica will be under continuous revision and expect the results will improve within the next several years.”
“The color of your skin can have a huge impact in how quickly you are triaged and managed for brain injury. People of color have significantly worse outcomes after traumatic brain injury than people who are white.” -- Uzma Samadani, founder of Oculogica
Powell thinks the EyeBOX could be an important complement to other concussion assessments.
“The Oculogica product is a viable diagnostic tool that supports clinical decision making. However, concussion is an injury that can present with a wide array of symptoms, and the use of technology such as the Oculogica should always be a supplement to patient interaction.”
Ioannis Mavroudis, a consultant neurologist at Leeds Teaching Hospital, agrees that the EyeBOX has promise, but cautions that concussions are too complex to rely on the device alone. For example, not all concussions affect how eyes move. “I believe that it can definitely help, however not all concussions show changes in eye movements. I believe that if this could be combined with a cognitive assessment the results would be impressive.”
The Oculogica team submitted their clinical data for FDA approval and received it in 2018. Now, they’re working to bring the test to the commercial market and using the device clinically to help diagnose concussions for clients. They also want to look at other areas of brain health in the next few years. Samadani believes that the EyeBOX could possibly be used to detect diseases like multiple sclerosis or other neurological conditions. “It’s a completely new way of figuring out what someone’s neurological exam is and we’re only beginning to realize the potential,” says Samadani.
One of Samadani’s biggest aspirations is to help reduce inequalities in healthcare because of skin color and other factors like money or language barriers. From that perspective, the EyeBOX’s greatest potential could be in emergency rooms. It can help diagnose concussions in addition to the questionnaires, assessments and symptom checklists, currently used in the emergency departments. Unlike these more subjective tests, EyeBOX can produce an objective analysis of brain injury through AI when patients are admitted and assessed, unrelated to their socioeconomic status, education, or language abilities. Studies suggest that there are racial disparities in how patients with brain injuries are treated, such as how quickly they're assessed and get a treatment plan.
“The color of your skin can have a huge impact in how quickly you are triaged and managed for brain injury,” says Samadani. “As a result of that, people of color have significantly worse outcomes after traumatic brain injury than people who are white. The EyeBOX has the potential to reduce inequalities,” she explains.
“If you had a digital neurological tool that you could screen and triage patients on admission to the emergency department you would potentially be able to make sure that everybody got the same standard of care,” says Samadani. “My goal is to change the way brain injury is diagnosed and defined.”