ABSTRACT
AIM: The present study evaluated with myocardial perfusion SPECT (MPS) the diagnostic accuracy of an artificial intelligence-enabled vectorcardiography system (Cardisiography, CSG) for detection of perfusion abnormalities. METHODS: We studied 241 patients, 155 with suspected CAD and 86 with known CAD who were referred for MPS. The CSG was performed after the MPS acquisition. The CSG results (1) p-factor (perfusion, 0: normal, 1: mildly, 2: moderately, 3: highly abnormal) and (2) s-factor (structure, categories as p-factor) were compared with the MPS scores. The CSG system was not trained during the study. RESULTS: Considering the p-factor alone, a specificity of >78% and a negative predictive value of mostly >90% for all MPS variables were found. The sensitivities ranged from 17 to 56%, the positive predictive values from 4 to 38%. Combining the p- and the s-factor, significantly higher specificity values of about 90% were reached. The s-factor showed a significant correlation (p=0.006) with the MPS ejection fraction. CONCLUSIONS: The CSG system is able to exclude relevant perfusion abnormalities in patients with suspected or known CAD with a specificity and a negative predictive value of about 90% combining the p- and the s-factor. Since it is a learning system there is potential for further improvement before routine use.
