Evidence map›Paper›PMID 35408254›Full record

ArticleSensors (Basel, Switzerland)2022

Non-Invasive Muscular Atrophy Causes Evaluation for Limb Fracture Based on Flexible Surface Electromyography System.

Xiachuan Pei, Ruijian Yan, Guangyao Jiang, Tianyu Qi, Hao Jin, Shurong Dong, Gang Feng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Wearable Bioelectronics for Home-Based Monitoring and Treatment of Muscle Atrophy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Xiachuan PeiKey Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
Ruijian YanDepartment of Orthopedic Surgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.ORCID 0000-0003-1572-6675
Guangyao JiangDepartment of Orthopedic Surgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.
Tianyu QiDepartment of Orthopedic Surgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.
Hao JinKey Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0001-9798-5149
Shurong DongKey Laboratory of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0002-8715-7072
Gang FengDepartment of Orthopedic Surgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.

Funding

the Joint Construction Project of Zhejiang province and Health Ministry WKJ-ZJ-2029the Key Research and Development Program of Zhejiang Province No. 2021C03108the Key Research and Development Program of Zhejiang Province No. 2021C04G2013057the National Key R&D Program of China 2018YFA0701400
6 · The paper itself

Abstract

Muscular atrophy after limb fracture is a frequently occurring complication with multiple causes. Different treatments and targeted rehabilitation procedures should be carried out based on the causes. However, bedside evaluation methods are invasive in clinical practice nowadays, lacking reliable non-invasive methods. In this study, we propose a non-invasive flexible surface electromyography system with machine learning algorithms to distinguish nerve-injury and limb immobilization-related atrophy. First, a flexible surface electromyography sensor was designed and verified by in vitro tests for its robustness and flexibility. Then, in vivo tests on rats proved the reliability compared with the traditional invasive diagnosis method. Finally, this system was applied for the diagnosis of muscular atrophy in 10 patients. The flexible surface electromyography sensor can achieve a max strain of 12.0%, which ensures close contact with the skin. The in vivo tests on rats show great comparability with the traditional invasive diagnosis method. It can achieve a high specificity of 95.28% and sensitivity of 98.98%. Application on patients reaches a relatively high specificity of 89.44% and sensitivity of 91.94%. The proposed painless surface electromyography system can be an easy and accurate supplementary for bedside muscular atrophy causes evaluation, holding excellent contact with the body.

Indexed as

Fractures, BoneMuscular AtrophyAlgorithmsAnimalsElectromyographyHumansRatsReproducibility of Resultsflexible systemmachine learningmuscular atrophysurface electromyography

Identifiers

PMID35408254
PMCPMC9003361

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.