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一个用于预测脑瘫儿童骨科干预后步态表现的模拟平台。

: A Simulation Platform to Predict Gait Performance Following Orthopedic Intervention in Children With Cerebral Palsy.

作者信息

Pitto Lorenzo, Kainz Hans, Falisse Antoine, Wesseling Mariska, Van Rossom Sam, Hoang Hoa, Papageorgiou Eirini, Hallemans Ann, Desloovere Kaat, Molenaers Guy, Van Campenhout Anja, De Groote Friedl, Jonkers Ilse

机构信息

Department of Movement Sciences, KU Leuven, Leuven, Belgium.

Department of Rehabilitation Sciences, Doctoral School of Biomedical Sciences, KU Leuven, Leuven, Belgium.

出版信息

Front Neurorobot. 2019 Jul 17;13:54. doi: 10.3389/fnbot.2019.00054. eCollection 2019.

Abstract

Gait deficits in cerebral palsy (CP) are often treated with a single-event multi-level surgery (SEMLS). Selecting the treatment options (combination of bony and soft tissue corrections) for a specific patient is a complex endeavor and very often treatment outcome is not satisfying. A deterioration in 22.8% of the parameters describing gait performance has been reported and there is need for additional surgery in 11% of the patients. Computational simulations based on musculoskeletal models that allow clinicians to test the effects of different treatment options before surgery have the potential to drastically improve treatment outcome. However, to date, no such simulation and modeling method is available. Two important challenges are the development of methods to include patient-specific neuromechanical impairments into the models and to simulate the effect of different surgical procedures on post-operative gait performance. Therefore, we developed the SimCP framework that allows the evaluation of the effect of different simulated surgeries on gait performance of a specific patient and includes a graphical user interface (GUI) that enables performing virtual surgery on the models. We demonstrated the potential of our framework for two case studies. Models reflecting the patient-specific musculoskeletal geometry and muscle properties are generated based solely on data collected before the treatment. The patient's motor control is described based on muscle synergies derived from pre-operative EMG. The GUI is then used to modify the musculoskeletal properties according to the surgical plan. Since SEMLS does not affect motor control, the same motor control model is used to define gait performance pre- and post-operative. We use the capability gap (CG), i.e., the difference between the joint moments needed to perform healthy walking and the joint moments the personalized model can generate, to quantify gait performance. In both cases, the CG was smaller post- then pre-operative and this was in accordance with the measured change in gait kinematics after treatment.

摘要

脑性瘫痪(CP)的步态缺陷通常采用单阶段多水平手术(SEMLS)进行治疗。为特定患者选择治疗方案(骨骼和软组织矫正的组合)是一项复杂的工作,而且治疗效果往往不尽人意。据报道,描述步态表现的参数中有22.8%出现恶化,11%的患者需要再次手术。基于肌肉骨骼模型的计算模拟能够让临床医生在手术前测试不同治疗方案的效果,有可能显著改善治疗效果。然而,迄今为止,尚无此类模拟和建模方法。两个重要的挑战是开发将患者特定的神经力学损伤纳入模型的方法,以及模拟不同手术程序对术后步态表现的影响。因此,我们开发了SimCP框架,该框架可以评估不同模拟手术对特定患者步态表现的影响,并包括一个图形用户界面(GUI),能够在模型上进行虚拟手术。我们通过两个案例研究展示了我们框架的潜力。仅根据治疗前收集的数据生成反映患者特定肌肉骨骼几何形状和肌肉特性的模型。根据术前肌电图得出的肌肉协同作用来描述患者的运动控制。然后使用GUI根据手术计划修改肌肉骨骼特性。由于SEMLS不影响运动控制,因此使用相同的运动控制模型来定义术前和术后的步态表现。我们使用能力差距(CG),即进行健康行走所需的关节力矩与个性化模型能够产生的关节力矩之间的差异,来量化步态表现。在这两个案例中,术后的CG均小于术前,这与治疗后步态运动学的测量变化一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/96dd/6650580/d9c3745525b1/fnbot-13-00054-g0001.jpg

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