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Autonomous FEs (AFE) - A stride toward personalized medicine
Computers & Mathematics with Applications ( IF 2.9 ) Pub Date : 2020-04-03 , DOI: 10.1016/j.camwa.2020.03.012
Zohar Yosibash , Kent Myers , Nir Trabelsi , Amir Sternheim

Finite element analysis (FEA), introduced more than half a century ago, requires a (qualified) analyst to generate the necessary input data, verify the output and post process the analysis results for a meaningful conclusion. The required expertise and labor efforts have precluded the use of FEA in daily medical practice by orthopedic surgeons for example.

Patient-specific analyses of the mechanical response of human bones may have a tremendous impact in clinical practice should they be easily accessible by orthopedic surgeons. Recent scientific advancements such as low dose CT scans, machine learning, and high order FEA which facilitates an inherent methodology for assessing numerical accuracy allow a fully autonomous process for assessing bone strength and fracture risk. This autonomous process, that we shall refer to here as “Autonomous Finite Element” (AFE) analysis, introduces a paradigm shift in the use of FEA.

We shall describe herein a novel process that utilizes AFE to produce a patient-specific assessment of bone strength. The process consists of an automatic segmentation of femurs from CT-scans by convolution neural networks, an automatic mesh generation and application of boundary conditions based on anatomical points, a high-order FE analysis with numerical error control, and finally an automatic report with a clear assessment of bone fracture risk. One specific application of AFE is the determination of the risk of fracture for patients with tumors of the femur and whether a prophylactic surgery is needed. To the best of our knowledge this is the first CE accredited AFE application being used by orthopedic surgeons in clinical practice.



中文翻译:

自主FE(AFE)-向个性化医学迈进

半个多世纪前提出的有限元分析(FEA)要求(合格的)分析师生成必要的输入数据,验证输出并对分析结果进行后处理,以得出有意义的结论。所需的专业知识和努力工作已排除了FEA在整形外科医师的日常医疗实践中的使用。

如果骨科医生能够轻松进行针对患者骨骼的机械反应的患者特定分析,则可能对临床实践产生巨大影响。低剂量CT扫描,机器学习和高阶FEA等最新科学进展促进了评估数值准确性的内在方法,使评估骨骼强度和骨折风险的过程得以完全自动化。这种自治过程,我们在这里称为“自治有限元”(AFE)分析,引入了使用FEA的模式转变。

我们将在本文中描述一种利用AFE进行患者特定骨强度评估的新颖方法。该过程包括通过卷积神经网络从CT扫描中自动分割股骨,基于解剖学点自动生成网格并应用边界条件,具有数值误差控制的高阶有限元分析以及最终的带有明确评估骨折风险。AFE的一种特殊应用是确定股骨肿瘤患者的骨折风险,以及是否需要进行预防性手术。据我们所知,这是整形外科医师在临床实践中使用的首个获得CE认证的AFE应用。

更新日期:2020-04-20
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