Key result
Artificial intelligence software for calcium scoring demonstrated an excellent correlation with expert human reading (Pearson R = 0.996), although 14% of patients had their category reclassified.
Why the study?
AI applied to cardiac imaging may improve processing and precision, but its accuracy and correlation with expert CT reading for CAC scoring and CAC-DRS classification required evaluation.
Does artificial intelligence software accurately calculate CAC scores and assign CAC-DRS classifications compared to expert human reading?
Cross-Sectional (n=100)
blinded
blinded randomization
Does artificial intelligence software accurately calculate CAC scores and assign CAC-DRS classifications compared to expert human reading?
Effect estimate: Pearson coefficient R = 0.996
AI software for CAC scoring shows excellent correlation with expert human reading, though minor discrepancies exist at very low calcium volumes (CAC=0 vs 1).
BACKGROUND: Artificial intelligence (AI) applied to cardiac imaging may provide improved processing, reading precision and advantages of automation. Coronary artery calcium (CAC) score testing is a standard stratification tool that is rapid and highly reproducible. We analyzed CAC results of 100 studies in order to determine the accuracy and correlation between the AI software (Coreline AVIEW, Seoul, South Korea) and expert level-3 computed tomography (CT) human CAC interpretation and its performance when coronary artery disease data and reporting system (coronary artery calcium data and reporting system) classification is applied. METHODS: A total of 100 non-contrast calcium score images were selected by blinded randomization and processed with the AI software versus human level-3 CT reading. The results were compared and the Pearson correlation index was calculated. The CAC-DRS classification system was applied, and the cause of category reclassification was determined using an anatomical qualitative description by the readers. RESULTS: The mean age was age 64.5 years, with 48% female. The absolute CAC scores between AI versus human reading demonstrated a highly significant correlation (Pearson coefficient R = 0.996); however, despite these minimal CAC score differences, 14% of the patients had their CAC-DRS category reclassified. The main source of reclassification was observed in CAC-DRS 0-1, where 13 were recategorized, particularly between studies having a CAC Agatston score of 0 versus 1. Qualitative description of the errors showed that the main cause of misclassification was AI underestimation of right coronary calcium, AI overestimation of right ventricle densities and human underestimation of right coronary artery calcium. CONCLUSION: Correlation between AI and human values is excellent with absolute numbers. When the CAC-DRS classification system was adopted, there was a strong correlation in the respective categories. Misclassified were predominantly in the category of CAC = 0, most often with minimal values of calcium volume. Additional algorithm optimization with enhanced sensitivity and specificity for low values of calcium volume will be required to enhance AI CAC score utilization for minimal disease. Over a broad range of calcium scores, AI software for calcium scoring had an excellent correlation compared to human expert reading and in rare cases determined calcium missed by human interpretation.
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Aldana-Bitar et al. (2023) conducted a cross-sectional in Coronary artery calcium (n=100). Artificial intelligence software (Coreline AVIEW) vs. Expert level-3 CT human reading was evaluated on Correlation between AI software and expert level-3 CT human CAC interpretation (Pearson coefficient R = 0.996). Artificial intelligence software for calcium scoring demonstrated an excellent correlation with expert human reading (Pearson R = 0.996), although 14% of patients had their category reclassified.
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