Purpose: To assess the ability of deep convolutional neural networks (DCNNs) to predict coronary artery calcium (CAC) and cardiovascular risk on chest radiographs. Materials and Methods: In this retrospective study, 1689 radiographs in patients who underwent cardiac CT and chest radiography within the same year, between 2013 and 2018, were included (mean age, 56 years ± 11 [standard deviation]; 969 radiographs in women). Agatston scores were used as ground truth labels for DCNN training on radiographs. DCNNs were trained for binary classification of (a) nonzero or zero total calcium scores, (b) presence or absence of calcium in each coronary artery, and (c) total calcium scores above or below varying thresholds. Results from classification of test images were compared with established 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores in each cohort. Classifier performance was measured using area under the receiver operating charac-teristic curve (AUC) with attention maps to highlight areas of decision-making. Results: Binary classification between zero and nonzero total calcium scores reached an AUC of 0.73 on frontal radiographs, with similar performance on laterals (AUC, 0.70; P =.56). Performance was similar for binary classification of absolute total calcium score above or below 100 (AUC, 0.74). Frontal radiographs that tested positive for a predicted nonzero CAC score correlated with a higher 10-year ASCVD risk of 17.2% ± 10.9 compared with 11.9% ± 10.2 for a negative test, indicating predicted CAC score of zero (P <.001). Multivariate logistic regression demonstrated the algorithm could predict a nonzero calcium score independent of traditional cardiovascular risk factors. Performance was reduced for individual coronary arteries. Heat maps primarily localized to the cardiac silhouette and occasionally other cardiovascular findings. DCNNs trained on chest radiographs had modest accuracy for predicting the presence of CAC correlating with cardiovas-Conclusion: cular risk.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging