Training an Artificial Intelligence Model for Aortic Dissection Detection Using Non-Contrast Computed Tomography...

Aortic dissection (AD) is an extreme consequence of impaired vascular remodeling homeostasis and requires rapid, accurate identification in clinical practice. This protocol describes an artificial intelligence-based learning model for AD identification utilizing non-contrast computed tomography (CT). Chest CT and aortic CT angiography datasets were collected from AD and non-AD patients at a Grade A tertiary hospital. Vascular structures on each axial image were manually segmented and annotated using the open-source software LabelMe to establish a segmentation dataset for model development and evaluation. The dataset was partitioned into training, test, and validation sets at an 8:1:1 ratio for model training and validation. Following the development of a model with robust detection performance, an online processing platform was constructed to visualize and present the results effectively. This approach provides a powerful, intelligent tool for rapid, preliminary screening of AD and addresses the unmet clinical need for accessible early detection across diverse clinical environments.