Machine Learning-based Algorithm Enables the Exclusion of Obstructive Coronary Artery Disease in the Patients Who Underwent Coronary Artery Calcium Scoring

Published:December 13, 2019DOI:

      Rationale and Objectives

      An application of artificial intelligence to screen for obstructive coronary artery disease (CAD) after coronary artery calcium scoring (CACS) test.

      Materials and Methods

      As an initial step we analyzed a group of 435 patients (23% male, mean age 61 ± 10) with low to moderate probability of CAD, who underwent clinically indicated CACS and coronary computed tomography angiography. Based on those data we elaborated a gradient boosting machine (GBM) model for prediction of obstructive CAD. Later the model was evaluated on a control group of 126 consecutive patients (31% male, mean age 59 ± 10).


      Stratified 10-fold cross-validation performed on the group of 435 patients demonstrated the GBM model's sensitivity at 100 ± 0% and specificity at 69.8 ± 3.6%, while the outcomes (confusion matrix) of a clinical application on the group of 126 patients were: 73 true negative, 0 false negative, 20 true positive, and 33 false positive.


      The GBM algorithm showcased a considerably high discriminatory power for excluding the presence of obstructive CAD, with negative predictive value and positive predictive value of 100% and 38%, respectively.

      Key Words


      Ao (areas of aortic root, thoracic ascending and descending aorta), AoV (aortic valve), ARC (aortic root calcification), CAC (coronary artery calcium), CACS (coronary artery calcium scoring test), CaSc (coronary artery calcium score based on Agatston method), CCTA (coronary computed tomography angiography), Cx (circumflex coronary artery), Eq (mass equivalent score), GBM (gradient boosting machine), LAD (left anterior descending), LM (left main), N (number of calcifications), RCA (right coronary artery), TAC (thoracic (ascending and descending) aortic calcification), Vol (volume score)
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