A Volumetric Analysis of Coronary Calcification on Non-Electrocardiogram-Gated Chest Computed Tomography Using Commercially Available Deep-Learning Artificial Intelligence

Objective: We examined the accuracy of coronary calcification volume (CV) measurements using deep-learning artificial intelligence (AI) for non-electrocardiogram (ECG)-gated chest computed tomography (CT) and compared it with CV measured a commercially available workstation (WS) Agatston score (AS). showed potential, limitations, optimization AI evaluating artery disorders.Materials methods: Overall, 315 344 patients were analyzed. All underwent non-ECG-gated ECG-gated non-enhanced CT during preoperative screening and/or pain assessment from March 7, 2021, to 2022. The CV-AI was that CV-WS AS. Stratification grades based on Cases mismatched stratification determine limitations AI. cut-off value best obtained.Results: correlation coefficients between AS 0.964 (p < 0.01) 0.960 0.01), respectively. risks significant consistency methods categories matched in 81.0% cases. When regarded as “gold standard”, accuracy, sensitivity, specificity, negative positive predictive values, Dice Jaccard indices 0.946, 0.921, 1, 0.856, 0.959, rarely overlooked calcifications underestimate risks. values categorization 10-100-360 (default: 10-100-500).Conclusion: has sufficient potential stratify risk events CT, particularly non-cardiac patients. However, results analyses should not be blindly accepted.