Unmet Need:
Eye movement frequently inform diagnoses of neurological dysfunctions and states. For example, such signatures may help gauge response to therapy, assess intoxication, and identify brain injury. A method is needed to predict and detect predict subtle eye movements for such applications.
Technology Overview:
Inventors at Johns Hopkins have developed a machine learning algorithm for home-based automated diagnosis of neurologic disease. By detecting various eye movement types such as nystagmus, saccades, and smooth pursuit, this information can be utilized to diagnosis neurologic conditions.
Stage of Development:
Proof of concept.
Publications:
N/A
