
Mayo Clinic AI Software Helps Diagnose Endocarditis
The Artificial Neural Network mimics cognitive brain function and reacts to accumulated knowledge. Evaluating data from 189 patients, the system correctly identified 72 of 73 implant-related infections and 12 of 13 endocarditis cases.
Researchers from the Mayo Clinic have developed software that is designed to mimic the human brain and assist physicians in diagnosing cardiac infections without an invasive exam.
Study author M. Rizwan Sohail, MD, an infectious diseases specialist at the Mayo Clinic, said that if the software works as hoped, it can be used to “help determine a percentage of endocarditis diagnoses with a high rate of accuracy,” thus saving “a significant number of patients from the discomfort, risk, and expense of the standard diagnostic procedure.”
During the study, the research team used the ANN to evaluate data from 189 patients with device-related endocarditis. The system correctly identified 72 of 73 implant-related infections and 12 of 13 endocarditis cases, with a confidence level greater than 99 percent, inpatients with a known diagnosis of endocarditis. The news release also notes that “when used on an overall sample that included both known and unknown cases, the ANN accurately excluded endocarditis in at least half of the cases, thus eliminating half the cohort from a needless invasive procedure.”
Results from this study were presented Saturday at the
More about Artificial Neural Networks and Artificial Intelligence in Healthcare
Newsletter
Stay informed and empowered with Medical Economics enewsletter, delivering expert insights, financial strategies, practice management tips and technology trends — tailored for today’s physicians.




















