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

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.