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.”
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.