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Additive manufacturing applications in orthopaedics: A review

机译:骨科中增材制造的应用:综述

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摘要

The applications of Additive Manufacturing (AM) have increased extensively in the area of orthopaedics. The AM applications are for making anatomic models, surgical instruments & tool design, splints, implants and prosthesis. A brief review of various research articles shows that patient-specific orthopaedic procedures provide multiple applications areas and provide directions for future developments. The purpose of this paper is to identify the best possible usage of additive manufacturing applications in orthopaedics field. It also presents the steps used to prepare a 3D printed model by using this technology and details applications in the field of orthopaedics. AM gives a flexible solution in orthopaedics area, where customised implants can be formed as per the required shape and size and can help substitution with customised products. A 3D model created by this technology gain an accurate perception of patient's anatomy which is used to perform mock surgeries and is helpful for highly complex surgical pathologies. It makes surgeon's job accessible and increases the success rate of the operation. AM provides a perfect fit implant for the specific patient by unlimited geometric freedom. Various scanning technologies capture the status of bone defects, and printing of the model is done with the help of this technology. It gives an exact generation of a physical model which is also helpful for medical education, surgical planning and training. This technology can help to solve present-day challenges as data of every patient is different from another.
机译:增材制造(AM)在整形外科领域的应用已广泛增加。 AM应用程序用于制作解剖模型,手术器械和工具设计,夹板,植入物和假体。对各种研究文章的简要回顾表明,针对患者的骨科手术提供了多个应用领域,并为未来的发展提供了指导。本文的目的是确定骨科领域增材制造应用的最佳可能用途。它还介绍了使用该技术准备3D打印模型的步骤,并详细介绍了骨科领域的应用。 AM为整形外科领域提供了灵活的解决方案,在该领域中,可以根据所需的形状和尺寸来定制植入物,并可以帮助替换定制产品。通过该技术创建的3D模型可准确了解患者的解剖结构,该模型可用于进行模拟手术,并有助于高度复杂的手术病理。它使外科医生的工作变得容易,并提高了手术的成功率。 AM通过无限的几何自由度为特定患者提供了完美的植入物。各种扫描技术都可以捕获骨缺损的状态,并且借助该技术可以完成模型的打印。它给出了物理模型的精确生成,这也有助于医学教育,手术计划和培训。由于每个患者的数据都不相同,因此该技术可以帮助解决当前的挑战。

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