Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms
Current state-of-the-art and utilities of machine learning for detection, monitoring, growth prediction, rupture risk assessment, and post-surgical management of abdominal aortic aneurysms
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DOI：
10.1016/j.apples.2022.100097
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发表时间：
2022-06-01
期刊：
影响因子：
--
通讯作者：
Arzani, Amirhossein
中科院分区：
文献类型：
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作者：
Baek, Seungik;Arzani, Amirhossein
超声成像长期以来一直在检测腹主动脉瘤（AAA）中发挥核心作用。随着近年来AAA患病率下降的趋势，仅建议65至75岁有既往吸烟史的男性进行超声筛查，目前美国不建议对女性进行全国性的筛查计划。在2000年代，几个研究小组证明了使用患者特定图像进行有限元应力分析的实用性，这对于准确评估破裂风险是有希望的，但是物理模型仍然需要通过考虑患者的变异性和多物理特征来增强。本综述旨在提供对新兴和替代技术以及新方法的调查，例如个性化医疗和数据驱动方法，这些技术和方法可能在检测小AAA、随访期间监测患者、预测AAA生长、评估破裂风险以及AAA患者管理的术后预后方面取得潜在突破。
Ultrasound imaging has long been playing a central role in detecting abdominal aortic aneurysms (AAAs). With a recent trend of reducing prevalence of AAAs, ultrasound screening is only recommended for men aged 65 to 75 years with previous smoking history, and a national level of a screening program for women is currently not recommended in the US. In the 2000s, several research groups demonstrated the utility of finite element stress analysis using patient-specific images, which was promising for an accurate assessment of the rupture risk, but physical models remain to be enhanced by considering patient variability and multi-physical characteristics. This review aims to provide a survey of emerging and alternative technologies and new methodologies, such as personalized medicine and data-driven approaches, that may make potential breakthroughs on detection of small AAAs, monitoring of patients during the follow-ups, prediction of AAA growth, assessment of the rupture risk, and post-surgical prognosis for AAA patient management.