Proposal for Machine Learning in Vestibular Project
By extracting the information more efficiently and accurately from the test results, machine learning techniques help clinicians get closer to the diagnosis and therefore make better decisions of treatment.
Abstract
Background Patients with reduced vestibular function suffer imbalance, spatial disorientation and blurred vision. Further, the problems may lead to various degrees of disability. However, large amounts of patients can not be diagnosed with the clinical tests available, such as Electronystamography tests, rotational tests, that are said to be the gold standard tests; more precisely, they can’t be diagnosed with the current analysis of the test results. By extracting the information more efficiently and accurately from the test results, machine learning techniques help clinicians get closer to the diagnosis and therefore make better decisions of treatment.