Digital twin and artificial intelligence technologies for predictive planning of endovascular procedures

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Jean-Noël Albertini , Lucie Derycke , Antoine Millon , Raphael Soler
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Abstract

Current planning of aortic and peripheral endovascular procedures is based largely on manual measurements performed from the 3-dimensional reconstruction of preoperative computed tomography scans. Assessment of device behavior inside patient anatomy is often difficult, and available tools, such as 3-dimensional–printed models, have several limitations. Digital twin (DT) technology has been used successfully in automotive and aerospace industries and applied recently to endovascular aortic aneurysm repair. Artificial intelligence allows the treatment of large amounts of data, and its use in medicine is increasing rapidly. The aim of this review was to present the current status of DTs combined with artificial intelligence for planning endovascular procedures. Patient-specific DTs of the aorta are generated from preoperative computed tomography and integrate aorta mechanical properties using finite element analysis. The same methodology is used to generate 3-dimensional models of aortic stent-grafts and simulate their deployment. Post processing of DT models is then performed to generate multiple parameters related to stent-graft oversizing and apposition. Machine learning algorithms allow parameters to be computed into a synthetic index to predict Type 1A endoleak risk. Other planning and sizing applications include custom-made fenestrated and branched stent-grafts for complex aneurysms. DT technology is also being investigated for planning peripheral endovascular procedures, such as carotid artery stenting. DT provides detailed information on endovascular device behavior. Analysis of DT-derived parameters with machine learning algorithms may improve accuracy in predicting complications, such as Type 1A endoleaks.

预测性规划血管内手术的数字孪生和人工智能技术
目前主动脉和外周血管内手术的规划主要基于术前计算机断层扫描三维重建的人工测量。评估患者解剖结构内的设备行为通常比较困难,现有的工具(如三维打印模型)也有一些局限性。数字孪生(DT)技术已成功应用于汽车和航空航天工业,最近又被应用于血管内主动脉瘤修复。人工智能可以处理大量数据,其在医学中的应用正在迅速增加。本综述旨在介绍将 DT 与人工智能相结合用于规划血管内手术的现状。根据术前计算机断层扫描生成患者特异性主动脉 DT,并通过有限元分析整合主动脉机械特性。同样的方法也用于生成主动脉支架移植物的三维模型并模拟其部署。然后对 DT 模型进行后处理,生成与支架移植物过大和贴合相关的多个参数。机器学习算法可将参数计算为合成指数,以预测 1A 型内漏风险。其他规划和尺寸应用包括为复杂动脉瘤定制的栅栏式和分支支架移植物。DT 技术还正在研究用于规划外周血管内手术,如颈动脉支架植入术。DT 可提供有关血管内设备行为的详细信息。利用机器学习算法分析 DT 衍生参数可提高预测并发症(如 1A 型内漏)的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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