Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and minimize radiation exposure. These anatomydependent parameters are organized into customizable organ programs, but manual selection of the programs increases workload and complexity. Our research introduces a deep learning algorithm that autonomously detects three target anatomies:the left coronary artery (LCA), right coronary artery (RCA), and left ventricle (LV),based on singleX-ray frames without vessel structure and enables adjustment of imaging parameters by choosing the appropriate organ program. We compared three deep-learning architectures: ResNet-50 for image data, a Multilayer Perceptron (MLP) for angulation data, and a multimodal approach combining both. The dataset for training and validation included 275 radiographic sequences from clinical examinations, incorporating coronary angiography, left ventriculography, and corresponding C-arm angulation, using only the first non-contrast frame of the sequence for the possibility of adapting the system before the actual contrast injection. The dataset was acquired from multiple sites, ensuring variation in acquisition and patient statistics. An independent test set of 146 sequences was used for evaluation. The multimodal model outperformed the others, achieving an average F1 score of 0.82 and an AUC of 0.87, matching expert evaluations. The model effectively classified cardiac anatomies based on pre-contrast angiographic frames without visible coronary or ventricular structures. The proposed deep learning model accurately predicts cardiac anatomy for cine acquisitions, enabling the potential for quick and automatic selection of imaging parameters to optimize image quality and reduce radiation exposure. This model has the potential to streamline clinical workflows, improve diagnostic accuracy, and enhance safety for both patients and operators.