Artificial intelligence-powered solutions for automated aortic diameter measurement in computed tomography: a narrative review.

Abstract

Background and objective: Patients with thoracic aortic aneurysm and dissection (TAAD) are often asymptomatic but present acutely with life threatening complications that necessitate emergency intervention. Aortic diameter measurement using computed tomography (CT) is considered the gold standard for diagnosis, surgical planning, and monitoring. However, manual measurement can create challenges in clinical workflows due to its time-consuming, labour-intensive nature and susceptibility to human error. With advancements in artificial intelligence (AI), several models have emerged in recent years for automated aortic diameter measurement. This article aims to review the performance and clinical relevance of these models in relation to clinical workflows.

Methods: We performed literature searches in PubMed, Scopus, and Web of Science to identify relevant studies published between 2014 and 2024, with the focus on AI and deep learning aortic diameter measurements in screening and diagnosis of TAAD.

Key content and findings: Twenty-four studies were retrieved in the past ten years, highlighting a significant knowledge gap in the field of translational medicine. The discussion included an overview of AI-powered models for aortic diameter measurement, as well as current clinical guidelines and workflows.

Conclusions: This article provides a thorough overview of AI and deep learning models designed for automatic aortic diameter measurement in the screening and diagnosis of thoracic aortic aneurysms (TAAs). We emphasize not only the performance of these technologies but also their clinical significance in enabling timely interventions for high-risk patients. Looking ahead, we envision a future where AI and deep learning-powered automatic aortic diameter measurement models will streamline TAAD clinical management.