医学
无症状的
动脉瘤
危险分层
放射科
临床实习
疾病
重症监护医学
外科
内科学
物理疗法
作者
Emmanuel Mensah,Catherine Pringle,Gareth Roberts,Nihal Gurusinghe,Aprajay Golash,Andrew F. Alalade
标识
DOI:10.1016/j.wneu.2022.02.006
摘要
Intracranial aneurysms are a common asymptomatic vascular pathology, the rupture of which is a devastating event with a significant risk of morbidity and mortality. Aneurysm detection and risk stratification before rupture events are, therefore, imperative to guide prophylactic measures. Artificial intelligence has shown great promise in the management pathway of aneurysms, through automated detection, the prediction of rupture risk, and outcome prediction after treatment. The complementary use of these programs, in addition to clinical practice, has demonstrated high diagnostic and prognostic accuracy, with the potential to improve patient outcomes. In the present review, we explored the role and limitations of deep learning, a subfield of artificial intelligence, in the aneurysm patient journey. We have also briefly summarized the application of deep learning models in automated detection and prediction in cerebral arteriovenous malformations and Moyamoya disease.
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