Columbia University researchers are working on a new diagnostic tool that could improve the detection of concussions. The effort is led by Thomas Bottiglieri, DO, chief of the Primary Care Sports Medicine division in the Department of Orthopedics at ColumbiaDoctors and head medical team physician for Columbia University Athletics.
Bottiglieri’s interest in concussion diagnostics stems from his own experience as a college football player who was medically retired after repeated brain injuries. He now cares for athletes with similar injuries and seeks to advance how they are diagnosed.
Current methods rely heavily on self-reporting of symptoms and clinical evaluation, both of which can be subjective. Research indicates many concussions go undiagnosed because athletes may avoid reporting symptoms that could sideline them.
“We measure electrocardiograms and cardiac enzymes to determine whether someone with chest pain has had a heart attack, but we have no clinically usable biomarkers to help us accurately identify concussion,” Bottiglieri says.
Bottiglieri initially worked on refining the vestibular ocular motor screening (VOMS) tool, which examines eye movements but lacks standardized thresholds for diagnosis. Interpretation depends on clinician experience.
He then collaborated with Linus Sun, a neuro-ophthalmologist, to find clearer indicators. Sun asked if Bottiglieri had noticed abnormal head movements in concussion patients—a phenomenon linked to cerebellum injury but not previously examined in this context.
Christopher Driscoll, who joined the project while a graduate student in Sun’s lab and had prior experience managing a division I football team, began developing code to isolate these subtle head movements. The team discovered an almost imperceptible oscillation in concussion patients’ heads, which became central to their diagnostic approach.
Driscoll created a machine learning algorithm to analyze recordings of these oscillations, measuring their frequency and amplitude. Using this technology, the lab built a biometric database with data from over 200 patients—including controls without concussion—to establish normal and abnormal cutoffs. Early results suggest it can distinguish between those with and without concussion, gauge injury severity, and detect chronic issues.
“Unlike other assessments that require interpretation, our approach gives us either a ‘yes’ or ‘no’ answer, and it can’t be gamed by players,” Bottiglieri says. “It will not only help us identify athletes and other individuals with concussion who need treatment but may also be used to determine when a patient is ready to return to normal activities,” Bottiglieri says.
The exam takes less than five minutes and requires no special training; it runs on standard laptops for use in clinics or athletic settings. The researchers aim to publish their findings and explore commercial opportunities for the technology while expanding its application as a potential screening tool for neurodegenerative diseases.
“Having an objective and easy-to-use tool to help identify concussion in athletes and others with head injury will be a real advance in the field,” Bottiglieri says.
Columbia University filed a provisional patent application for this technology in 2025.
