Real-Time Precision Tracking System in Periprosthetic Acetabular Osteotomy With Osteotome Chisel Elastic Deformation Consideration.

IF 1.7 4区 医学 Q4 BIOPHYSICS
Yumei Li, Yang Han, Gang Fu, Yanjie Xu, Tianmu Wang, Zhenguo Nie
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引用次数: 0

Abstract

The periprosthetic acetabular osteotomy (PAO) is a commonly used technique in orthopedics for treating developmental hip dysplasia and hip dislocation, as the most effective treatment for developmental dysplasia of the hip (DDH). However, performing PAO can be challenging for surgeons due to limited visibility and difficulty in detecting any deformations of osteotome chisels when they are deeply immersed in the pelvis. These challenges can result in serious complications, such as excessive bleeding and nerve injuries. We propose a novel precision tracking system to mitigate these risks by acquiring the chisel deformation in real-time. This system consists of a newly designed osteotome chisel with five built-in microsensors, which are finely chosen with the help of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We propose a fast finite element method (FFEM) model to calculate the deformation of the chisel from flexibility information collected by these five sensors, where the model deformation can be predicted from a well-designed light deep neural network (DNN) model. Our model has achieved an impressive R2 value of 0.98781 and an average deformation error of only 0.07 mm in nodes compared to the experiment. The prediction time of FFEM model has been shortened to 0.33 s, and the total time including three-dimensional reconstruction and visualization has been shortened to 3.84 s. Implementing such an osteotome chisel with a deformation tracking system has shown immense potential in increasing surgical accuracy and reducing medical negligence for PAO operations.

考虑骨凿弹性变形的髋臼假体周围截骨术实时精确跟踪系统。
假体周围髋臼截骨术(PAO)是骨科治疗发育性髋关节发育不良和髋关节脱位的常用技术,是治疗发育性髋关节发育不良(DDH)最有效的方法。然而,对于外科医生来说,由于能见度有限,当骨凿深度浸入骨盆时,很难检测到任何变形,因此进行PAO是具有挑战性的。这些挑战可能导致严重的并发症,如出血过多和神经损伤。我们提出了一种新的精确跟踪系统,通过实时获取凿子变形来减轻这些风险。该系统由一个新设计的骨凿和五个内置微传感器组成,这些微传感器是在理想溶液相似度偏好排序技术(TOPSIS)的帮助下精细选择的。我们提出了一种快速有限元方法(FFEM)模型,根据这五个传感器收集的柔性信息计算凿子的变形,其中模型变形可以通过精心设计的轻型深度神经网络(DNN)模型进行预测。与实验相比,我们的模型获得了令人印象深刻的R2值0.98781,节点平均变形误差仅为0.07 mm。FFEM模型预测时间缩短至0.33 s,包括三维重建和可视化在内的总时间缩短至3.84 s。采用这种带有变形跟踪系统的骨凿,在提高手术精度和减少PAO手术的医疗疏忽方面显示出巨大的潜力。
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
3.40
自引率
5.90%
发文量
169
审稿时长
4-8 weeks
期刊介绍: Artificial Organs and Prostheses; Bioinstrumentation and Measurements; Bioheat Transfer; Biomaterials; Biomechanics; Bioprocess Engineering; Cellular Mechanics; Design and Control of Biological Systems; Physiological Systems.
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