Role of Artificial Intelligence in Unruptured Intracranial Aneurysm: An Overview

被引:8
|
作者
Marasini, Anurag [1 ]
Shrestha, Alisha [2 ]
Phuyal, Subash [3 ]
Zaidat, Osama O. [4 ,5 ]
Kalia, Junaid Siddiq [1 ,6 ,7 ]
机构
[1] AINeuroCare Acad, Dallas, TX 75203 USA
[2] Travel & Mt Med Ctr, Kathmandu, Nepal
[3] Upendra Devkota Mem Natl Inst Neurol & Allied Sci, Dept Neurointervent Radiol, Kathmandu, Nepal
[4] St Vincent Mercy Med Ctr, Dept Endovasc Neurosurg, Toledo, OH USA
[5] St Vincent Mercy Med Ctr, Dept Stroke, Toledo, OH USA
[6] VeeOne Hlth Inc, Clin Strategy, Roseville, CA 95661 USA
[7] Neurologypocketbookcom, Dallas, TX USA
来源
FRONTIERS IN NEUROLOGY | 2022年 / 13卷
关键词
artificial intelligence; machine learning; aneurysm rupture; unruptured intracranial aneurysms (UIAs); deep learning;
D O I
10.3389/fneur.2022.784326
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
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.
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页数:6
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