Artificial intelligence using a deep learning versus expert computed tomography human reading in calcium score and coronary artery calcium data and reporting system classification

Aldana-Bitar, Jairo; Cho, Geoffrey W.; Anderson, Lauren; Karlsberg, Daniel W.; Manubolu, Venkat S.; Verghese, Dhiran; Hussein, Luay; Budoff, Matthew J.; Karlsberg, Ronald P.
Coronary Artery Disease
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In this study, the researchers aimed to evaluate the accuracy and correlation between an artificial intelligence (AI) software, Coreline AVIEW from Seoul, South Korea, and expert human interpretation in assessing coronary artery calcium (CAC) scores using non-contrast calcium score images. A total of 100 studies were randomly selected and processed by both the AI software and human level-3 computed tomography (CT) reading. The results demonstrated a highly significant correlation between the AI software and human reading in terms of absolute CAC scores. However, despite minimal score differences, 14% of the patients experienced reclassification in the coronary artery disease data and reporting system (CAC-DRS) category. The main cause of reclassification was observed in the CAC-DRS 0-1 category, where misclassification was influenced by factors such as AI underestimation of right coronary calcium, AI overestimation of right ventricle densities, and human underestimation of right coronary artery calcium. Overall, the study highlights the strong correlation between AI and human values in CAC scoring, with the need for further algorithm optimization to improve AI sensitivity and specificity, particularly for minimal disease detection. The AI software showed excellent correlation with human expert reading across a broad range of calcium scores and occasionally identified calcium that was missed by human interpretation.