A new test called the EyeBox could provide a more objective - and portable - tool to measure whether people have concussions in stadiums and hospitals.
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.”
Artificial Intelligence is already helping to grow some of the food we eat.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Researchers from the University of Cambridge have laid the foundations for growing synthetic embryos that could develop a beating heart, gut and brain.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
