SCINA: a case-based reasoning system for the interpretation of myocardial perfusion scintigrams

The purpose of this study was to utilize case-based reasoning techniques of artificial intelligence to develop a computer-based image interpretation system which automatically derives a comprehensive assessment concerning the presence, location, and severity of coronary artery disease from SPECT myocardial perfusion scintigraphy. Image analysis software was developed on a Macintosh computer. Polar map analysis of the tracer distribution in the myocardium was used to derive an input data set for the case-based reasoner. A case library of 100 patients who had been submitted to coronary angiography and perfusion scintigraphy was complied by a retrospective data base search. Sensitivity and specificity for detection of CAD were 83% and 70%. Sensitivity and specificity for localization of disease were 70% and 60% for the left anterior descending, 75% and 65% for the left circumflex artery and 53% and 63% for the right coronary artery. These results indicate that the case-based reasoning method permits the development of a clinically useful automated interpretation system for myocardial perfusion scintigraphy.