骨科手术术前规划与运动分析的新兴创新技术。

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery.

作者信息

Berhouet Julien, Samargandi Ramy

机构信息

Service de Chirurgie Orthopédique et Traumatologique, Centre Hospitalier Régional Universitaire (CHRU) de Tours, 1C Avenue de la République, 37170 Chambray-les-Tours, France.

Equipe Reconnaissance de Forme et Analyse de l'Image, Laboratoire d'Informatique Fondamentale et Appliquée de Tours EA6300, Ecole d'Ingénieurs Polytechnique Universitaire de Tours, Université de Tours, 64 Avenue Portalis, 37200 Tours, France.

出版信息

Diagnostics (Basel). 2024 Jun 21;14(13):1321. doi: 10.3390/diagnostics14131321.

Abstract

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

摘要

近年来,术前规划取得了重大进展,其具有双重重点:提高植入物放置的准确性和增强功能结果的预测。通过开发用于三维术前图像的先进处理方法,这些突破得以实现。这些方法不仅提供了新颖的可视化技术,还能无缝集成到计算机辅助设计模型中。此外,运动捕捉系统的改进在这一进展中发挥了关键作用。这些“无标记”系统实施起来更简单,便于进行数据分析。同时,利用人工智能的机器学习算法的出现,实现了解剖学和功能数据的融合,从而为患者制定高度个性化的术前计划。术前规划从二维向三维、从静态向动态的转变与技术进步密切相关,本指导性综述将对此进行描述。最后,将引入涵盖关节周围软组织的四维规划概念,作为骨科手术领域的前瞻性发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/80a0/11240316/92d31a0e9454/diagnostics-14-01321-g001.jpg

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