Chinese Experts Group of Metaverse Empowers Patient Education

通信作者 白春学，教授、主任医师 . Tel： 021-64041990. E-mail： bai.chunxue@zs-hospital.sh.cn

［收稿日期］ 2024-09-20 ［接受日期］ 2024-09-29 ［发表日期］ 2024-12-30 

［基金项目］ 上海市科学技术委员会项目基金（21DZ2200600），国家自然科学基金（82170110），上海市浦江人才计划（20PJ1402400），上海市健康科普人才能力提升专项（青年英才）（JKKPYC- 2023-A20），2020 年度上海工程技术研究中心建设项目（20DZ2254400），福建省自然科学（2022D014）. Supported by Fund of Shanghai Municipal Commission of Science and Technology (21DZ2200600), National Natural Science Foundation of China (82170110), Shanghai Pujiang Talent Program (20PJ1402400), Project of Promoting Ability of Medical Science Popularization for Young Talents in Shanghai (JKKPYC-2023-A20), Project of Establishment of Shanghai Engineering Technology Research Center in 2020 (20DZ2254400), Natural Science Foundation of Fujian Province (2022D014).

伦理声明 无

DOI: https://doi.org/10.61189/186518otugjv

指南与共识 |Published on: 30 December 2024

[Metaverse in Medicine] 2024; 1 (4): 54-61

王源¹，王利新²，白浩鸣³，余情³，耿文叶⁴，戴伟辉⁵，白春学^1,6,7,8,9，高承实¹⁰，张宇鸣¹¹，杨达伟^1,6,7,8,9*1. 复旦大学附属中山医院呼吸与危重症医学科，上海 2000322. 复旦大学附属中山医院血管外科，上海 2000323. 复旦大学附属中山医院教育处，上海 2000324. 复旦大学张江科技研究院，上海 2000005. 复旦大学管理学院，上海 2000006. 复旦大学附属中山医院（厦门）呼吸与危重症医学科，厦门 3610007. 上海呼吸物联网医学工程技术研究中心，上海 2000328. 上海市呼吸病研究所，上海 2000329. 中国肺癌防治联盟，上海 20003210. 上海散列信息科技合伙企业，上海 200000

11. 中国信息通信研究院医疗健康大数据与网络研究中心（华东），上海 200000

［作者简介］ 王 源，硕士生. E-mail： wangyuan011022@163.com* 通信作者（Corresponding author）. Tel: 021-64041990, E-mail：yang.dawei@zs-hospital.sh.cn

［收稿日期］ 2024-12-12 ［接受日期］ 2024-12-29 ［发表日期］ 2024-12-30

［基金项目］ 上海市健康科普人才能力提升专项（青年英才）（JKKPYC-2023-A20），上海高校市级重点课程（FDSHZD202409），上海市科学技术委员会上海工程技术研究中心建设计划（20DZ2254400）. Supported by Project of Promoting Ability of Medical Science Popularization for Young Talents in Shanghai （JKKPYC-2023-A20）， Shanghai Municipal Key Courses （FDSHZD202409）， Fund of Shanghai Municipal Commission of Science and Technology（ 20DZ2254400）

伦理声明 无。利益冲突 所有作者声明不存在利益冲突。作者贡献 王源： 撰写文章；王利新、白皓鸣、余情、耿文叶、戴伟辉、白春学、高承实、张宇鸣、杨达伟： 模型设计、文章修改。

DOI：https://doi.org/10.61189/317754bkfwnp

专题报道 |Published on: 30 December 2024

[Metaverse in Medicine] Volume 1, Number 4, 17-20

专家述评AI时代医学知识创新与传播方式的重构⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 高承实（1）

专题报道：元宇宙医学教育虚实共生：元宇宙驱动下运动医学教育生态的数字化重构与治理优化⋅⋅⋅⋅⋅⋅⋅ 赵修涵，刘宗玉，牛海涛（6）5A MedAgent： 面向患者教育的医疗大语言模型多智能体协作系统⋅⋅⋅⋅⋅⋅⋅⋅⋅ 周介立，周禄庭，辛弘毅（12）住培元宇宙临床思维训练模式探索⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 王源，王利新，白浩鸣，等（17）元宇宙技术在冠状动脉慢性闭塞病变介入治疗教学中的应用与发展⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 檀亚航，张涛，赵林（21）AI平台在元宇宙与未来医学课程中应用的初步探讨⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 王源，杨达伟（23）综述辅助康复外骨骼机器人研究现状及元宇宙赋能研究的展望⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 朱子睿，王源，杨达伟（26）元宇宙技术在脑卒中患者康复治疗中的作用⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 张振鹏，王源，杨达伟（32）方法学研究

云加端OSA诊治训练新模式⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 陆俊羽，蒋维芃，白春学（37）

伦理与法规虚拟现实技术中的身心健康风险与伦理治理路径：多维度视角的探索⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 朱林蕃，王国豫（43）指南与共识元宇宙赋能患者教育中国专家共识⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 元宇宙赋能患者教育中国专家组（54）摘要解读Virtual reality in the management of patients with low back and neck pain：a retrospective analysis of 82 people treated solely in the metaverse 摘要及解读⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅钱芃欣（62）

Development of metaverse for intelligent healthcare 摘要及解读⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 刘晓静，陈智鸿（63）

State-of-the-art human-computer-interaction in metaverse 摘要及解读⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 肖顺周，朱涛（64）

CONTENTS IN BRIEF 目次具体目次请下载PDF查看。

Please download the PDF to check the CONTENTS IN BRIEF.

《元宇宙医学》（Metaverse in Medicine；ISSN: 3006-4236）由复旦大学附属中山医院

期刊中心与Zentime出版社联合出版。

|Published on: 30 December 2024

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.

Research Article |Published on: 31 December 2024

[Progress in Medical Devices] 2024; 2 (4): 144-152

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.

Review Article |Published on: 31 December 2024

[Progress in Medical Devices] 2024; 2 (4): 153-160

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.

Review Article |Published on: 31 December 2024

[Progress in Medical Devices] 2024; 2 (4): 161-173

Kai Wang¹ , Zhe Zhang¹ , Mingling Wang² , Shiming Feng³ , Huanjia Xue¹ , Xiang Huan¹ , Liwei Wang¹ 

¹Department of Anesthesiology, ²Operating Room, ³Department of Orthopaedics, Xuzhou Clinical College Affiliated  to Xuzhou Medical University, Xuzhou 221009, Jiangsu, China. 

Address correspondence to: Liwei Wang, Department of Anesthesiology, Xuzhou Central Hospital, No.199 Jiefang South Road, Quanshan District, Xuzhou 221009, Jiangsu, China. Email:  760020230115@xzhmu.edu.cn.

Acknowledgement: This work was supported by Young Scientist Fund of National Natural Science Foundation of  China (81700078) and Xuzhou Medical Key Talents program (XWRCHT20220051). 

DOI: https://doi.org/10.61189/143336qedqgl

Received November 2, 2024; Accepted February 14, 2025; Published March 31, 2025

Highlights

● Training with the multi-objective teaching model significantly improved in perioperative skills of residents. 

● Multi-objective teaching model effectively facilitates the acquisition of comprehensive theoretical knowledge in   anesthesiology. 

● Multi-objective teaching model emphasizes the development of teamwork skills.

Research Article |Published on: 31 March 2025

[Perioperative Precision Medicine] 2025; 3 (1): 9-15

Fei Liu¹, Haipo Cui¹, Fujia Sun², Shuhao Hou³, Peng Yue⁴ 

Schools of ¹Health Science and Engineering, ²Mechanical Engineering, ³Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China. ⁴Shanghai Guoyan Medical Device Testing Cen ter Co., Ltd., Shanghai 200000, China. 

Address correspondence to: Haipo Cui, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.334, Jungong Road, Shanghai 200093, China. Tel: +86 21-55271290, E-mail: hpcui@usst.edu.cn; Fujia Sun, School of Mechanical Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. Tel: +86 13621773624, E-mail: chinasfj@126.com.

DOI: https://doi.org/10.61189/273582nrnxmc

Received August 12, 2024; Accepted September 11, 2024; Published March 31, 2025

Highlights 

 ● Algorithms such as adaptive beamforming and synthetic aperture technology have significantly improved the quality of ultrasound images. 

 ● New algorithms, such as deep learning, can adapt to more complex signal environments at the expense of real-time performance. 

 ● Combining different algorithms can overcome the limitations of a single algorithm, thereby improving image resolution, contrast, and noise resistance.

Review Article |Published on: 31 March 2025

[Progress in Medical Devices] 2025; 3 (1): 26-42

Yilong Chen, Lin Mao, Zijie Zhou, Chengli Song 

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

Address correspondence to: Lin Mao, Shanghai Institute for Minimally Invasive Therapy, School of  Health Science and Engineering, University of Shanghai for Science and Technology, Yangpu District,  Shanghai 200093, China. Tel: +86-21-55572159. E-mail: linmao@usst.edu.cn.

DOI: https://doi.org/10.61189/314845qnicsc

Received January 19, 2025; Accepted February 19, 2025; Published March 31, 2025

Highlights

● Continuous suturing in traditional manual suturing shortens operation time and reduces infection risk. Absorbable sutures are preferred for intestinal suturing and anastomosis to minimize foreign body reactions.

● Mechanical anastomosis with linear and circular metal staples offers distinct advantages, while new biodegradable staples demonstrate good performance. 

● Magnetopressure anastomosis, leveraging magnetic attraction, has shown success in specific scenarios, providing innovative approaches to intestinal anastomosis. 

● Radio frequency energy tissue welding technology enables rapid, seamless intestinal anastomosis, with   fewer complications and holds strong potential for future applications. 

● The support method for intestinal anastomosis, particularly the "degradable internal stent anastomosis" using a simple support method, shows significant promise in animal studies.

Review Article |Published on: 31 March 2025

[Progress in Medical Devices] 2025; 3 (1): 66-76

Wangzheqi Zhang¹, Chenglong Zhu¹, Xingzhi Liao², Feixiang Wu³, Hui Chen⁴, Wenyun Xu⁵, Jinlong Qu⁶, Miao Zhou⁷, Jinfei Shi⁸, Liangqing Lin⁹, Shengyun Cai¹⁰, Wenchao Gao¹¹, Hua Tang¹², Ying Huang¹³, Zui Zou¹

¹School of Anesthesiology, Naval Medical University, Shanghai 200433, China. ²Department of Anesthesiology, 904th Hospital of The Joint Logistics Support Force of the PLA, Wuxi 214044, Jiangsu, China. ³Department of An-esthesiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China. ⁴De-partment of Anesthesiology and Perioperative medicine, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China. ⁵Department of Anesthesiology, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China. ⁶Department of Emergency and Critical Care Medicine Second Affili-ated Hospital of Naval Medical University, Shanghai 200003, China. ⁷Department of Anesthesiology, the Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University, Nanjing 210009, Jiangsu, China. ⁸Department of Anesthesiology, Anhui Provincial Hospital of the Armed Police, Hefei 230001, China. ⁹Department of Anesthesiology, The First Hospital of Putian City, Teaching Hospital of Fujian Medical University, Putian 351100, Fujian, China. ¹⁰Department of Obstetrics and Gynecology, First Affiliated Hospital of Naval Medical University, Shanghai 200433, China. ¹¹Department of Col-orectal Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai 200003, China. ¹²Department of Thoracic Surgery, The Second Affiliated Hospital of Naval University, Shanghai 200003, China. ¹³Department of Intensive Care Unit, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China. 

Address correspondence to: Hua Tang, Department of Thoracic Surgery, The Second Affiliated Hospital of Naval University, Shanghai 200003, China. Email: Tangh_mits@163.com. Ying Huang, Department of Intensive Care Unit, the Affiliated Huaian No.1 People’s Hospital of Nanjing Medical University, Huaian, Jiangsu 223300, China. Email: huangying5249@163.com. Zui Zou, School of Anesthesiology, Naval Medical University, 168 Changhai Road, Shanghai 200433, China. Email: zou-zui@smmu.edu.cn.

Acknowledgement: This study was funded by the following projects: Shanghai Industrial Collaborative Innovation Project (HCXBCY - 2023 – 041, XTCX - KJ - 2024 - 39, HCXBCY - 2024 – 033), 2024 Basic Medicine Innovation Open Topic (JCKFKT - MS - 002), and the 2024 Annual Pharmaceutical Science and Technology Key Research Project of the China Medicine Education Association (2024KTZ011).

DOI: https://doi.org/10.61189/447393ljoyfh

Received January 20, 2025; Accepted February 20, 2025; Published March 31, 2025

Expert Consensus |Published on: 31 March 2025

[Perioperative Precision Medicine] 2025; 3 (1): 1-8.