Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening - Winkelmann, Moritz T.; Jacoby, Johann; Schwemmer, Chris; Faby, Sebastian; Krumm, Patrick; Artzner, Christoph; Bongers, Malte N. (RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2022)
Purpose Evaluation of machine learning-based fully automated artery-specific coronary artery calcium (CAC) scoring software, using semi-automated software as a reference.Methods A total of 505 patients underwent non-contrast-enhanced calcium scoring computed tomography (CSCT). Automated, machine learning-based software quantified the Agatston score (AS), volume score (VS), and mass score (MS) of each coronary artery [right coronary artery (RCA), left main (LM), circumflex (CX) and left anterior descending (LAD)]. Identified CAC of readers who annotated the data with semi-automated software served as a reference standard. Statistics included comparisons of evaluation time, agreement of identified CAC, and comparisons of the AS, VS, and MS of the reference standard and the fully automated algorithm.Results The machine learning-based software correlated strongly with the reference standard for the AS, VS, and MS (Spearmanʼs rho > 0.969) (p < 0.001), with excellent agreement (ICC > 0.919) (p < 0.001). The mean assessment time of the reference standard was 59 seconds (IQR 39–140) and that of the automated algorithm was 5.9 seconds (IQR 3.9–16) (p < 0.001). The Bland-Altman plots mean difference and 1.96 upper and lower limits of agreement for all arteries combined were: AS 0.996 (1.33 to 0.74), VS 0.995 (1.40 to 0.71), and MS 0.995 (1.35 to 0.74). The mean bias was minimal: 0.964–1.0429. Risk class assignment showed high accuracy for the AS in total (weighed κ = 0.99) and for each individual artery (κ = 0.96–0.99) with corresponding correct risk group assignment in 497 of 505 patients (98.4 %).Conclusion The fully automated artery-specific coronary calcium scoring algorithm is a time-saving procedure and shows excellent correlation and agreement compared with the clinically established semi-automated approach.Key points: Citation Format
Keywords: computed tomography; calcium scoring; coronary artery calcium; diagnostic accuracy; machine learning