AI-based algorithm for assessing coronary artery calcium score on contrast-enhanced cardiac computed tomography scans

Abstract

Abstract Introduction In cardiac computed tomography (CT) examinations, non-contrast scans are often performed in addition to contrast-enhanced scans to quantify the coronary artery calcification (CAC) score. These calcium score scans are associated with additional exposure to ionizing radiation. Purpose We sought to develop a fully automated artificial intelligence (AI)-based algorithm capable of determining CAC score based solely on contrast-enhanced CT scans, eliminating the need for additional non-contrast scans. Methods An automated CAC scoring algorithm on contrast-enhanced CT scans was developed and trained using a dataset of 297 cardiac CT studies. Coronary artery calcifications were manually segmented in contrast-enhanced scans using a threshold of 2 standard deviations above the mean attenuation value of the ascending aorta. On non-contrast scans, CAC was manually assessed using a standard threshold of ≥130 Hounsfield Units. A correction factor for calcium score results assessed on contrast-enhanced scans was determined using linear regression. The algorithm was tested on an independent set of 90 contrast-enhanced CT scans (four manufacturers, eight scanner models). We compared automated CAC scores to manual expert reader reference assessments on non-contrast CT scans. CAC scores were categorized into four risk categories following the Society of Cardiovascular Computed Tomography recommendations: 0, 1-100, 101-300, and >300 Agatston Units. Results In the assessment of 90 CT studies (mean age 61.0±11.4 years, 46.7% males), the AI-model detected CAC in 69 contrast-enhanced scans (76.7%), comparable to the human reader's detection rate of CAC in 71 non-contrast scans (78.9%) (p = 0.63). The CAC score was initially calculated using AI-based segmentation of coronary calcification on contrast-enhanced scans and then the result was multiplied by established linear correction factor of 1.97. There was an excellent correlation between AI-model and manual reference total CAC scores (Pearson’s r = 0.96, 95% CI 0.94–0.97, p < 0.001). The model correctly classified 77 patients (85.6%) into the same CAC risk category as the human reader (Figure 1). Among 19 patients (21.1%) with a CAC score of zero, only 1 patient (5.3%) was reclassified with a non-zero CAC score by the AI-model. Cohen’s kappa value for CAC score risk categorization was 0.80 (p < 0.001), indicating very good agreement (Figure 2). Bland–Altman analysis revealed minimal bias of -9.7 Agatston Unit with 95% limits of agreement ranging from -184.8 to 165.5 Agatston Unit. Conclusions CAC score can be accurately quantified on contrast-enhanced cardiac CT scans using an automated AI-based algorithm. This approach has the potential to eliminate the necessity for an additional non-contrast CT scan, thereby reducing the patient's exposure to ionizing radiation.