Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview

• Published in: Frontiers in neurology Vol. 13; p. 784326
• Main Authors: Marasini, Anurag, Shrestha, Alisha, Phuyal, Subash, Zaidat, Osama O., Kalia, Junaid Siddiq
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 23.02.2022
• Subjects: aneurysm rupture; artificial intelligence; deep learning; machine learning; Neurology; unruptured intracranial aneurysms (UIAs); unruptured intracranial aneurysms (UIAs); deep learning; machine learning; artificial intelligence; aneurysm rupture
• ISSN: 1664-2295; 1664-2295
• DOI: 10.3389/fneur.2022.784326

Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs.