A Bayesian approach for dealing with uncertainties in detection of coronary artery stenosis using a knowledge-based system

Abstract
A knowledge-based system that combines subjective Bayesian methods with rules specified by cardiologists to diagnose coronary artery stenosis from postexercise myocardial perfusion scintigrams is discussed. This expert system was used to determine which of the three main coronary arteries had the dominant stenosis. The system also indicated when a patient had a normal myocardial perfusion pattern (no stenosis). The system was run on a set of scans from 91 patients, and the results were compared with an existing expert system that uses the Dempster-Shafer theory of evidence for dealing with uncertainties. The system was able to determine the coronary artery with the dominant stenosis over 90% of the time when supplied with prior knowledge that all the patients have single-vessel stenosis. The system was also able to determine with good accuracy whether a patient had a stenosed coronary artery or normal myocardial perfusion when no prior information was available. The program can be used initially to screen out patients with normal scintigrams. Once the patients with normal scintigrams have been removed, the expert system can then be run on the remaining patients and utilize prior knowledge that they have stenosed coronary arteries. This improves the reliability of the diagnosis.<>