Project Description

Almost every Chiari patient knows that tonsillar herniation alone is not a good indicator of symptomatic Chiari. The question then becomes, is there another objective way to identify (or diagnose) symptomatic Chiari from MRIs?

Dr. Malena Espanol of the Mathematics Dept. at the University of Akron believes there is. Dr. Espanol will apply what is known as Machine Learning to the problem. The idea is to input a large amount of data – in this case morphometric measurements from the MRIs of Chiairi patients and healthy controls – into a computer analysis, so the computer can learn how to distinguish between symptomatic and asymptomatic Chiari.

Dr. Espanol’s preliminary results are very encouraging, so Conquer Chiari has awarded her a $33,000 grant to continue this exciting work. Developing an objective way to diagnose Chiari is one of Conquer Chiari’s top research priorities.