Artificial Intelligence-Based Predictive Modeling for Aortic Aneurysms.

Abdominal aortic aneurysms (AAAs) remain a major concern to the global society because of the associated risk of rupture and death. Currently, the management of AAAs entails clinical and imaging risk factors, which are not precise and accurate in terms of patient-specific risk assessment. Over the last decade, the utilization of artificial intelligence (AI) and machine learning (ML) algorithms has transformed the process of decision-making in the field of medicine by allowing for the creation of personalized models based on the patient's characteristics. This review aims to discuss the current state and future directions of AI in the form of predictive modeling for aortic aneurysms, stressing the versatility and progression of the ML approaches in risk assessment, screening, and prognosis. We expand on the various strategies used in AI-based solutions and the differences between general and specific approaches such as supervised and unsupervised learning, deep learning, and others. Furthermore, we bring forward the problem of incorporating clinical, imaging, and genomic data into AI/ML to improve its predictiveness and applicability to clinical practice. In addition, we discuss the difficulties and prospects of turning the developed AI-based forecasting models into clinical practice, as well as the problems associated with data quality, model explainability, and legal and ethical concerns. This review aims to reveal the opportunities of AI and ML in enhancing the risk assessment and management of AAAs to shift the paradigm of cardiovascular care toward precision medicine.