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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

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.

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.

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.

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

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.

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.

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.
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Most Read

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.

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.

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.

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.

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.

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.

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

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.

The application of mammography imaging in the diagnosis and prediction of breast diseases

Siyan Liu1,*, Guihua Wu2,*, Changjiang Zhou2,#, Shiju Yan1,#, Haipo Cui1 

1School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Department of Sonography, People's Hospital Affiliated to Shandong First Medical University, Jinan 271100, Shandong, China. 

*The authors contribute equally. 

#Address correspondence to: Changjiang Zhou, Department of Sonography, People's Hospital Affiliated to Shandong First Medical University, Changshao North Road, Laiwu District, Jinan 271100, China. E-mail: 390585866@ qq.com/jnsrmyybgs@jn.shandong.cn. Shiju Yan, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. Tel: 18217617984. E-mail: yanshiju@usst.edu.cn.

DOI: https://doi.org/10.61189/295735bbiagx

Received September 22, 2023; Accepted December 6, 2023; Published March 31, 2024

Highlights 

Computer-assisted detection, diagnosis, and prediction systems have played an effective role in diagnosing and treating female breast diseases and monitoring the course of disease. Especially in mammography imaging, they provide key support for the early diagnosis of breast cancer. This highlights the significance of modern technology in enhancing breast disease management and improving women's health.
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