Fully automated detection of anomalous aortic origin of the coronary arteries in coronary CT images: a novel artificial intelligence-based screening tool

Abstract Background Anomalous Aortic Origin of the Coronary Artery (AAOCA) is a rare congenital disease that can be easily overlooked during contrast-enhanced Coronary Computed Tomography Angiography (CCTA) image analysis. Objective In this study, we aimed to develop a novel artificial intelligence-based screening tool to automate the detection of AAOCA in CCTA images. Method We enrolled 127 patients from the coronary artery anomaly registry from two centers with AAOCA diagnosis based on CCTA as well as 413 normal cases. A deep learning model was developed to automatically segment the aorta and the left ventricle (LV), after which the images were automatically cropped to the aortic root and LV intersection with a box size of 6 cm3. The images were then clipped to Hounsfield Units ranging from -400 to 750 and normalized to -1 to1. These cropped regions were fed into different residual convolutional neural networks (ResNet, with different depths 10, 18, 34 and 50) to detect AAOCA. Model development (parameter and hyperparameters optimization) was performed on the training and validation sets (70/10%), and finally, it was tested on the 20% untouched test set. Various classification metrics were reported for the detection of AAOCA. Results ResNet-10 achieved an AUC of 0.91 (sensitivity: 0.89, specificity: 0.86), while ResNet-18 showed improved performance with an AUC of 0.95 (sensitivity: 0.89, specificity: 0.95). ResNet-34 achieved an AUC of 0.94 (sensitivity: 0.83, specificity: 0.96), and ResNet-50 excelled with the highest AUC of 0.96 (sensitivity: 0.89, specificity: 0.95), indicating its superior capability in accurately identifying AAOCA. Conclusion We developed a high-performance AI tool for the fully automated detection of AAOCA using CCTA images. This tool can be used to evaluate large patient cohorts and detect AAOCA in extensive databases. Moreover, it can operate seamlessly alongside clinical assessments of CCTA scans, providing real-time alerts to medical personnel about potential AAOCA findings. This enhances diagnostic efficiency and supports early intervention in cardiovascular care.