Meta-analysis of deep learning approaches for automated coronary artery calcium scoring: Performance and clinical utility AI in CAC scoring: A meta-analysis: AI in CAC scoring: A meta-analysis

Introduction: Manual Coronary Artery Calcium (CAC) scoring, crucial for assessing coronary artery disease risk, is time-consuming and variable. Deep learning, particularly through Convolutional Neural Networks (CNNs), promises to automate and enhance the accuracy of CAC scoring, which this study investigates. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a comprehensive literature search across PubMed, Embase, Web of Science, and IEEE databases from their inception until November 1, 2023, and selected studies that employed deep learning for automated CAC scoring. We then evaluated the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies 2. The main metric for evaluation was Cohen's kappa statistic, indicating an agreement between deep learning models and manual scoring methods. Results: A total of 25 studies were included, with a pooled kappa statistic of 83 % (95 % CI of 79 %–87 %), indicating strong agreement between automated and manual CAC scoring. Subgroup analysis revealed performance variations based on imaging modalities and technical specifications. Sensitivity analysis confirmed the reliability of the results. Conclusions: Deep learning models, particularly CNNs, have great potential for use in automated CAC scoring applications, potentially enhancing the efficiency and accuracy of risk assessments for coronary artery disease. Further research and standardization are required to address the major heterogeneity and performance disparities between different imaging modalities. Overall, our findings underscore the evolving role of artificial intelligence in advancing cardiac imaging and patient care.