A Systematic Review on Detection of Aortic Dissection by Utilizing Deep Learning

Abstract

Aortic dissection is a cardiovascular disease-causing mortality. It is vital to detect after emergently for earlier interventions and healing prognosis of aortic dissection. Computer-aided diagnosis and detection systems assist physicians and radiologists to identify a disease as a second opinion during decision-making for treatment. These systems provide early detection as well as saving on time and low cost when increasing accuracy in diagnosis. The systems utilize deep learning techniques to discover the localization of disease, and lesions by examining medical data. Thus, the purpose of this study is to perform systematic literature reviews in order to discover the studies that focus on aortic dissection detection by using deep learning. For this, Web of Science has been searched by combining specific keywords. Although 66 publications have been retrieved on the electronic database, only 53 publications have been examined due to the inclusion criteria. The findings presents that there is a tendency on improving a segmentation application to detect aortic dissection by employing deep learning techniques. This study will help to explore trends for new studies focusing on aortic dissection detection with deep learning technique. © 2025 Elsevier B.V., All rights reserved.