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使用电阻抗光谱法检测和监测人工关节周围感染:一项初步病例研究。

Detecting and Monitoring Periprosthetic Joint Infection by Using Electrical Bioimpedance Spectroscopy: A Preliminary Case Study.

作者信息

Balato Marco, Petrarca Carlo, Arpaia Pasquale, Moccaldi Nicola, Mancino Francesca, Carleo Giusy, Minucci Simone, Mariconda Massimo, Balato Giovanni

机构信息

Department of Electrical Engineering and Information Technologies (DIETI), University of Naples "Federico II", 80125 Napoli, Italy.

Interdepartmental Research Center on Management and Innovation in Healthcare, (CIRMIS), University of Naples "Federico II", 80131 Napoli, Italy.

出版信息

Diagnostics (Basel). 2022 Jul 10;12(7):1680. doi: 10.3390/diagnostics12071680.

Abstract

A method to detect the presence of infection after Total Joint Arthroplasty is presented. The method is based on Electrical Bioimpedance Spectroscopy and guarantees low latency, non-invasiveness, and cheapness with respect to the state of art. Experimental measurements were carried out on a singular patient who had already undergone bilateral Total Knee Arthroplasty. He was affected by a hematogenous Periprosthetic Joint Infections on the left knee. The right knee was adopted as the reference. Measurements were acquired once before the surgical procedure (Diagnosis Phase) and twice in the postoperative phases (Monitoring Phase). The most relevant frequency range, for diagnosis and monitoring phases, was found to be between 10 kHz to 50 kHz. The healing trend predicted by the decrease of impedance magnitude spectrum was reflected in clinical and laboratory results. In addition, one month after the last acquisition (two months after the surgery), the patient fully recovered, confirming the prediction of the Electrical Bioimpedance Spectroscopy technique.

摘要

本文提出了一种检测全关节置换术后感染情况的方法。该方法基于生物电阻抗谱技术,与现有技术相比,具有低延迟、非侵入性和低成本的特点。对一名已接受双侧全膝关节置换术的患者进行了实验测量。该患者左膝患有血源性假体周围关节感染,右膝作为对照。在手术前(诊断阶段)进行了一次测量,在术后阶段(监测阶段)进行了两次测量。结果发现,对于诊断和监测阶段,最相关的频率范围在10kHz至50kHz之间。通过阻抗幅值谱下降预测的愈合趋势与临床和实验室结果相符。此外,在最后一次测量后一个月(手术后两个月),患者完全康复,证实了生物电阻抗谱技术的预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/173c/9323083/70db4cec63e8/diagnostics-12-01680-g001.jpg

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