Detection of Aortic Dissection and Intramural Hematoma in Non-Contrast Chest Computed Tomography Using a You Only Look Once-Based Deep Learning Model

Background/Objectives: Aortic dissection (AD) and aortic intramural hematoma (IMH) are fatal diseases with similar clinical characteristics. Immediate computed tomography (CT) with a contrast medium is required to confirm the presence of AD or IMH. This retrospective study aimed to use CT images to differentiate AD and IMH from normal aorta (NA) using a deep learning algorithm. Methods: A 6-year retrospective study of non-contrast chest CT images was conducted at a university hospital in Seoul, Republic of Korea, from January 2016 to July 2021. The position of the aorta was analyzed in each CT image and categorized as NA, AD, or IMH. The images were divided into training, validation, and test sets in an 8:1:1 ratio. A deep learning model that can differentiate between AD and IMH from NA using non-contrast CT images alone, called YOLO (You Only Look Once) v4, was developed. The YOLOv4 model was used to analyze 8881 non-contrast CT images from 121 patients. Results: The YOLOv4 model can distinguish AD, IMH, and NA from each other simultaneously with a probability of over 92% using non-contrast CT images. Conclusions: This model can help distinguish AD and IMH from NA when applying a contrast agent is challenging.