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CC BY-NC-ND 4.0 · Nuklearmedizin 2024; 63(03): 213-218
DOI: 10.1055/a-2263-2322
Original Article

Diagnostic accuracy of artificial intelligence-enabled vectorcardiography versus myocardial perfusion SPECT in patients with suspected or known coronary heart disease

Diagnostische Genauigkeit der KI-basierten Vektorkardiografie im Vergleich zur Myokardperfusions-SPECT bei Patienten mit Verdacht auf KHK oder bekannter KHK
Simon Aydar
1   Institute of Radiology, Nuclearmedicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, Bad Oeynhausen, Germany
,
Hermann Knobl
1   Institute of Radiology, Nuclearmedicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, Bad Oeynhausen, Germany
,
Wolfgang Burchert
1   Institute of Radiology, Nuclearmedicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, Bad Oeynhausen, Germany
,
Oliver Lindner
1   Institute of Radiology, Nuclearmedicine and Molecular Imaging, Heart and Diabetes Center North Rhine-Westphalia, Bad Oeynhausen, Germany
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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.

Keywords

chronic coronary syndrome, myocardial perfusion SPECT, vectorcardiography, artificial intelligence


Publication History

Received: 20 September 2023

Accepted after revision: 08 January 2024

Article published online:
20 February 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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