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Home Progress in Medical Devices
ISSN: 2957-5478
Indexed in: Europub, CNKI, Crossref, Dimensions, Google Scholar
Editor-in-Chief: Haipo Cui
Email: PMD@zentimecorp.com
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Progress in Medical Devices (PMD) is an open-access, peer-reviewed online journal dedicated to the rapid publication of articles about various apparatus, machines, implants, software systems and in vitro reagents, such as medical robotics, catheter devices, minimally invasive devices, as well as medical device design and manufacturing processes. Articles from experts in this field will offer key insight in the areas of clinical practice, advocacy, education, administration, and research of medical devices.

 

PMD aims to show the progress in research, development and clinical use of medical devices that help to improve diagnostic, interventional and therapeutic performance and provide novel information that can be effective in reducing the complexity, lowering cost, or ameliorating adverse results of treatments.

 

Please join us in this open-access endeavor by submitting your high-quality papers for publication in PMD.

Lastest Issue

An investigation of upper extremity impedance modeling and sensory thresholds in envelope wave electrical stimulation

Renling Zou1, Yuhao Liu1, Yicai Wu1, Liang Zhao1, Jigao Dai1, Xiufang Hu1, Xuezhi Yin2 

1School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200000, China. 2Shanghai Berry Electronic Technology Co., Ltd., Shanghai, 200000, China.

Address correspondence to: Renling Zou, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200000, China. E-mail: zou renling@usst.edu.cn.

Acknowledgement: This work was supported by Science and Technology Commission of Shanghai Municipality (21S31906000), the National Natural Science Foundation of China (NSFC) Grant (61803265), and Medical-in dustrial cross-project of USST Grant (1022308524).

DOI: https://doi.org/10.61189/434505lacrsk

Received April 24, 2024; Accepted June 25, 2024; Published December 31, 2024 

Highlights 

 ● This study introduces a novel impedance model for the human upper limb, providing a highly accurate fit be tween frequency and impedance values. 

 ● The newly proposed Voltage Perception Threshold (VPT) method offers a more reliable measure of electrical stimulus sensation, independent of current magnitude and output frequency, compared to the traditional Current Perception Threshold (CPT).

Advancements in finite element analysis for prosthodontics

Yan Wang, Liwen Chen

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Liwen Chen, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: chenlw@usst.edu.cn.

DOI：https://doi.org/10.61189/974215qcjfzk

Highlights

● This paper presents a comprehensive review of the advancements in finite element analysis (FEA) within the field of prosthodontics over the past five years.● It examines the role of FEA in aiding the selection of restorative materials, enhancing prosthetic designs, and in vestigating the dynamic interactions between prostheses and natural dentition.● Integrating FEA findings with clinical practice enhances treatment outcomes and patient satisfaction.

Application and progress of functionalized magnetic bead-based biosensors for protein detection

Haoyuan Su1, Yuehua Liao2, Shu Wu1, Jun Ji1, Shuya An1, Dongdong Zeng2

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2School of Medical device, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.

Address correspondence to: Dongdong Zeng, School of Medical device, Shanghai University of Medicine & Health Sciences, No. 268 Zhouzhu Highway, Pudong, Shanghai 201318, China. E-mail: zengdd@sumhs.edu.cn.

DOI: https://doi.org/10.61189/403384jfzmyx

Highlights

● In the field of bioanalysis, a new biosensor technology based on functionalized magnetic beads is leading a new direction in protein detection. With its excellent separation efficiency and sensitivity, it provides a powerful tool for early disease diagnosis and biomarker monitoring.● This article explores the latest advancements in this technology, including innovative magnetic bead designs, diverse detection strategies, and the technical challenges and future development directions. It reveals the potential and application prospects of biosensor technology in biomarker detection.

Review of gait prediction of lower extremity exoskeleton robot

Haonan Geng1, Xudong Guo1, Haibo Lin1, Youguo Hao2, Guojie Zhang3

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Shanghai Putuo District People’s Hospital, Shanghai 200060, China. 3LingYuan Iron and Steel CO., LTD, Lingyuan 122500, Liaoning Province, China.

Address correspondence to: Xudong Guo, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. Email: guoxd@usst.edu.cn; Youguo Hao, Shanghai Putuo District Central Hospital, No.1291 Jiangning Road, Putuo District, Shanghai, 200060, China. Email: youguohao6@163.com.

DOI: https://doi.org/10.61189/673672yizrwd

Received September 8, 2024; Accepted November 6, 2024; Published December 31,2024

Highlights

●Gait prediction relies on multimodal sensor data, and the acquisition of multimodal information, such as physical sensors and bioelectrical signal sensors, is introduced in order to monitor and analyze the lower limb movement in real time, and provide a data basis for prediction.● The application of machine learning algorithms in gait prediction technology, such as Support Vector Machine, Random Forest, and Back Propagation Neural Network, is reviewed to construct an optimized gait prediction model, which provides effective support for the intelligent control of exoskeleton.● Compared with machine learning algorithms, the article summarizes the researchers’ efforts to extract and un derstand the hidden patterns in gait data by constructing neural network models related to different deep learning algorithms, which are used to improve the accuracy and robustness of gait prediction.

Overview of the current development in Visual-Inertial Systems

Mingxia Wei, Qingyun Meng

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Qingyun Meng, School of Health Science and Engineering, University of Shanghai for Science and Technology, Yangpu District, Shanghai 200093, China. Tel: +86-13761813609. E-mail: mengqy@sumhs.edu.cn.

DOI: https://doi.org/10.61189/521889ygxkmc

Received May 15, 2024; Accepted June 25, 2024; Published December 31, 2024

Highlights

● A comprehensive overview of Visual-Inertial Navigation Systems.● Exploration of key technologies, including image processing methods for visual odometry.

Online recognition method for walking patterns of intelligent knee prostheses based on CNN-LSTM algorithm

Yibin Zhang1, Yan Wang1, Hongliu Yu21School of Medical Devices, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. 2School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Hongliu Yu, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai city Jungong road 516, Shanghai 200093, China. E-mail: yhl98@hotmail.com.

DOI：https://doi.org/10.61189/961030gznunx

Received June 21, 2024; Accepted November 20, 2024; Published December 31, 2024

Highlights

● In prosthetics, using AI algorithms to identify the fused sensor data as known walking patterns has extremely strong expandability. Moreover, as the learning data continues to expand, the robustness of the model itself also increases accordingly.● There are numerous AI algorithms currently available. The effective utilization of algorithm combination techniques to learn from each other’s strengths can significantly improve the accuracy of identification. The combined model of convolutional neural networks (CNN) and bidirectional long short term memory (LSTM) attempted in this paper has witnessed a significant improvement in its comprehensive recognition rate.● In the practical application of prosthetics, the real-time performance during the mode switching transition period is particularly important as it can reflect the flexibility of the prosthetics. In this paper, the algorithm optimized by the AI model has controlled the delay rate within one gait cycle, greatly enhancing the safety and reliability of pro-sthetics in actual use.

Construction and comparative analysis of an early screening prediction model for fatty liver in elderly patients based on machine learning

Xiaolei Cai1*, Qi Sun2*, Cen Qiu2*, Zhenyu Xie1, Jiahao He2, Mengting Tu3, Xinran Zhang2, Yang Liu2, Zhaojun Tan2, Yutong Xie2, Xixuan He1, Yujing Ren1, Chunhong Xue1, Siqi Wang2, Linrong Yuan2, Miao Yu2, Xuelin Cheng4, Xiaopan Li4, Sunfang Jiang4, Huirong Zhu1

1Tangqiao Community Health Service Center, Shanghai 200127, China. 2Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. 3Shanghai DianJi University, Shanghai 201306, China. 4Health Man-agement Center, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China.*The authors contribute equally.

Address correspondence to: Sunfang Jiang, Health Management Center, Zhongshan Hospital Affiliated to Fudan University, Gate 5 East Campus, No. 179 Fenglin Road, Xuhui District, Shanghai 200032, China. Email: jiang.sunfang@zs-hospital.sh.cn. Huirong Zhu, Tangqiao Community Health Service Center, No.131 Pujian Road, Pudong New District, Shanghai 200127, China. Email: rachel1022@126.com.

DOI: https://doi.org/10.61189/568091unpkqk

Received May 11, 2024; Accepted July 16, 2024; Published September 30, 2024

Highlights

●This study collected three years of physical examination data from older adults in the Tangqiao community of Shanghai, which is more regionally representative.

●The most suitable model for this study was selected from six machine learning models to construct a fatty liver risk prediction model for the elderly.

●This study combines six feature selection algorithms with varying performance to screen the features most rele vant to fatty liver.

Analysis of urinary non-formed components at home based on machine learning algorithms

Yifei Bai, Rongguo Yan, Yuqing Yang, Chengang Mao

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Rongguo Yan, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. E-mail: yanrongguo@usst.edu.cn.

DOI: https://doi.org/10.61189/846307fkxccq

Received April 12, 2024; Accepted July 11, 2024; Published September 30, 2024

Highlights

●The study evaluated five machine learning algorithms in analyzing urinary non-formed components. Among them, the Random Forests model demonstrated the highest accuracy, precision, recall, and F1 score, suggesting its effectiveness in analyzing urinary non-formed components.

●A technological innovation is introduced for home urinalysis, offering the potential to enhance medical efficiency and patient experience.

Integrating Traditional Chinese Medicine massage therapy with machine learning: A new trend in future healthcare

Yichun Shen1, Shuyi Wang1, Yuhan Shen1, Hua Xing2

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200080, China.

Address correspondence to: Shuyi Wang, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. E-mail: wangshuyi@usst.edu.cn.

DOI: https://doi.org/10.61189/721472czacxf

Received April 12, 2024; Accepted July 11, 2024; Published September 30, 2024

Highlights

● Machine learning can enhance the individualization of treatment in Chinese massage.

● An intelligent system improves the efficiency of Traditional Chinese Medicine massage therapy.

● The integration of Traditional Chinese Medicine Massage Therapy with machine learning represents a new trend in future healthcare.

Optimization design and performance study of magnesium alloy vascular clamp

Weiwei Fan, Lin Mao, Bojun Liu, Chengli Song

Shanghai Institute for Minimally Invasive Therapy, School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, China.

Address correspondence to: Lin Mao, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. Tel: +86-21-55572159. E-mail: linmao@usst.edu.cn.

DOI: https://doi.org/10.61189/883654uegazz

Received March 21, 2024; Accepted June 5, 2024; Published September 30, 2024

Highlights

● A V-shaped vascular clamp featuring a locking mechanism and transverse teeth has been developed.

● Comparative analysis of clamps with various inner diameters reveals optimal closure with specific configurations.

● The designed clamp presents superior stress-strain response, robust clamping force, and consistent corrosion resistance.
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Most Read

Construction and comparative analysis of an early screening prediction model for fatty liver in elderly patients based on machine learning

Xiaolei Cai1*, Qi Sun2*, Cen Qiu2*, Zhenyu Xie1, Jiahao He2, Mengting Tu3, Xinran Zhang2, Yang Liu2, Zhaojun Tan2, Yutong Xie2, Xixuan He1, Yujing Ren1, Chunhong Xue1, Siqi Wang2, Linrong Yuan2, Miao Yu2, Xuelin Cheng4, Xiaopan Li4, Sunfang Jiang4, Huirong Zhu1

1Tangqiao Community Health Service Center, Shanghai 200127, China. 2Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. 3Shanghai DianJi University, Shanghai 201306, China. 4Health Man-agement Center, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China.*The authors contribute equally.

Address correspondence to: Sunfang Jiang, Health Management Center, Zhongshan Hospital Affiliated to Fudan University, Gate 5 East Campus, No. 179 Fenglin Road, Xuhui District, Shanghai 200032, China. Email: jiang.sunfang@zs-hospital.sh.cn. Huirong Zhu, Tangqiao Community Health Service Center, No.131 Pujian Road, Pudong New District, Shanghai 200127, China. Email: rachel1022@126.com.

DOI: https://doi.org/10.61189/568091unpkqk

Received May 11, 2024; Accepted July 16, 2024; Published September 30, 2024

Highlights

●This study collected three years of physical examination data from older adults in the Tangqiao community of Shanghai, which is more regionally representative.

●The most suitable model for this study was selected from six machine learning models to construct a fatty liver risk prediction model for the elderly.

●This study combines six feature selection algorithms with varying performance to screen the features most rele vant to fatty liver.

Review of methods for detecting electrode-tissue contact status during atrial fibrillation ablation

Mengying Zhan, Jiahao Zhang, Yuqiu Zhou, Qijun Xie, Fangfang Luo, Yu Zhou 

School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai  200093, China. 

 

Address correspondence to: Yu Zhou, School of Health Sciences and Engineering, University of  Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. Tel: +86- 18021042556. E-mail: zhouyu@usst.edu.cn.

DOI: https://doi.org/10.61189/650204jodubt 

Received January 29, 2024; Accepted March 25, 2024; Published September 30, 2024

Highlights

● The effect of electrode-tissue contact force on the efficacy and safety of ablation of atrial fibrillation was reviewed  in detail.

● The existing contact force sensing catheters on the market are compared and introduced.

● Three impedance-related methods for assessing catheter adherence are introduced.

Integrating Traditional Chinese Medicine massage therapy with machine learning: A new trend in future healthcare

Yichun Shen1, Shuyi Wang1, Yuhan Shen1, Hua Xing2

1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200080, China.

Address correspondence to: Shuyi Wang, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. E-mail: wangshuyi@usst.edu.cn.

DOI: https://doi.org/10.61189/721472czacxf

Received April 12, 2024; Accepted July 11, 2024; Published September 30, 2024

Highlights

● Machine learning can enhance the individualization of treatment in Chinese massage.

● An intelligent system improves the efficiency of Traditional Chinese Medicine massage therapy.

● The integration of Traditional Chinese Medicine Massage Therapy with machine learning represents a new trend in future healthcare.

Optimization design and performance study of magnesium alloy vascular clamp

Weiwei Fan, Lin Mao, Bojun Liu, Chengli Song

Shanghai Institute for Minimally Invasive Therapy, School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, China.

Address correspondence to: Lin Mao, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. Tel: +86-21-55572159. E-mail: linmao@usst.edu.cn.

DOI: https://doi.org/10.61189/883654uegazz

Received March 21, 2024; Accepted June 5, 2024; Published September 30, 2024

Highlights

● A V-shaped vascular clamp featuring a locking mechanism and transverse teeth has been developed.

● Comparative analysis of clamps with various inner diameters reveals optimal closure with specific configurations.

● The designed clamp presents superior stress-strain response, robust clamping force, and consistent corrosion resistance.

Analysis of urinary non-formed components at home based on machine learning algorithms

Yifei Bai, Rongguo Yan, Yuqing Yang, Chengang Mao

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Rongguo Yan, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. E-mail: yanrongguo@usst.edu.cn.

DOI: https://doi.org/10.61189/846307fkxccq

Received April 12, 2024; Accepted July 11, 2024; Published September 30, 2024

Highlights

●The study evaluated five machine learning algorithms in analyzing urinary non-formed components. Among them, the Random Forests model demonstrated the highest accuracy, precision, recall, and F1 score, suggesting its effectiveness in analyzing urinary non-formed components.

●A technological innovation is introduced for home urinalysis, offering the potential to enhance medical efficiency and patient experience.

Applications of vibration sensors in medicine: Enhancing healthcare through innovative monitoring

Zine Ghemari

Electrical Engineering Department, Mohamed Boudiaf University of M’sila, 28000, Algeria

Address correspondence to: Zine Ghemari, Electrical Engineering Department, Mohamed Boudiaf University of M’sila, P.O. Box 166, Ichbilya - M’Sila 28000, Algeria. E-mail: ghemari-zine@live.fr.

Acknowledgement: None.

DOI: https://doi.org/10.61189/871852usmbep

Received February 11, 2024; Accepted May 6, 2024; Published June 30, 2024

Highlights

● The article provides an overview of vibration sensors and their use in medical applications.

● Vibration sensors are used to monitor human movement, such as gait analysis and they can provide valuable data for assessing mobility, balance, and detecting abnormalities in movement patterns.

● The article explores how vibration sensors are integrated into wearable health devices, and these devices can monitor vital signs such as heart rate, respiratory rate, and sleep quality, providing continuous health monitoring

A comprehensive review of spike sorting algorithms in neuroscience

Wentao Quan1 , Youguo Hao2 , Xudong Guo1 , Peng Wang1 , Yukai Zhong3 

1 School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093,  China. 2 Putuo District People’s Hospital, Shanghai 200060, China. 3 Yangpu District Kongjiang Hospital, Shanghai  200082, China.

Address correspondence to: Youguo Hao, Putuo District People’s Hospital, No.1291 Jiangning Road, Putuo,  Shanghai 200060, China. Email: youguohao6@163.com.

Acknowledgement: This work was supported by the Science and Technology Innovation Plan of Shanghai Science  and Technology Commission (22S31902200).

DOI: https://doi.org/10.61189/016816myowlr

Received December 17, 2023; Accepted January 15, 2024; Published June 30, 2024

Highlights

● The detailed steps of spike sorting algorithm and the different algorithms used in each step are summarized. 

● The advantages and disadvantages of each step of spike sorting algorithm are compared. 

● The detailed application of deep learning technology in spike sorting is introduced.

Research progress of photoacoustic imaging technology in brain diseases

Tingting Shi, Rongguo Yan, Xinrui Gui, Ruoyu Song

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093,  China.

Address correspondence to: Rongguo Yan, Department of Biomedical Engineering, School of Health Science  and Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Yangpu, Shanghai  200093, China. E-mail: yanrongguo@usst.edu.cn.

DOI: https://doi.org/10.61189/579429fwpcmo

Received December 29, 2023; Accepted April 10, 2024; Published June 30, 2024

Highlights

● This review introduces the basic principles and features of photoacoustic imaging technology.

● This review illustrates the application of photoacoustic imaging in the study of brain diseases.

Research progress and clinical application of cooled radiofrequency ablation

Dandan Gu, Ruiyan Qian, Danni Rui, Difang Liu, Haitao Yao, Yifan Yang, Yu Zhou

School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai  200093, China.

Address correspondence to: Yu Zhou, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. Tel: 18021042556. E-mail: zhouyu@usst. edu.cn.

DOI: https://doi.org/10.61189/585036wxisob

Received December 4, 2023; Accepted January 8, 2024; Published June 30, 2024

Highlights:

● Cooled radiofrequency ablation (CRFA) represents an advancement in RF ablation, enhancing treatment safety and efficacy through electrode cooling. 

● CRFA principles, electrode cooling methods, efficacy evaluation, and an overview of major CRFA devices available on the market are comprehensively analyzed. 

● The clinical advancements in applying CRFA technology indicate its feasibility and safety as a viable treatment modality.

Advancements in irreversible electroporation ablation technology for treating atrial fibrillation

Binyu Wang, Tiantian Hu, Jiuzhou Zhao, Jincheng Xu, Banghong Chen, Yicheng Liu, Yu ZhouSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Yu Zhou, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Changbai New Village Street, Yangpu District, Shanghai 200093, China. Tel: 18021042556. E-mail: zhouyu@usst.edu.cn.

Acknowledgement: None.

DOI: https://doi.org/10.61189/758818obsmms

Received January 17 2024; Accepted February 7, 2024; Published June 30, 2024

Highlights

● Pulsed field ablation (PFA) demonstrates superior efficacy atrial fibrillation than traditional methods.

● Enhanced safety profile of PFA reduces complications in adjacent tissue damage.

● Optimized outcomes depends on key PFA parameters such as voltage, pulse width, and frequency.
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