33 KiB
附录
| 类别 | 名称 | |
| 发表论文 | 题目 | 影响因子 |
| Enhancement of TKI sensitivity in lung adenocarcinoma through m6A-dependent translational repression of Wnt signaling by circ-FBXW7 | 41.4 | |
| Stem signatures associating SOX2 antibody helps to define diagnosis and prognosis prediction with esophageal cancer | 5.3 | |
| 智能化辅助图像实时去雾技术在腹腔镜胆囊切除术中应用研究 | ||
| 增强现实、虚拟现实与混合现实在腔镜肝脏外科中的应用进展 | ||
| Intelligent Surgery Enters the Blind Spot of Lumpectomy Liver Resection | ||
| Intelligent digital fogging technology shows great potential in laparoscopic hepatectomy surgery | ||
| 基于精准医疗下肺癌类器官模型的研究 | ||
| Research on lung cancer organoid model based on precision medicine | ||
| Intelligent Surgical Confidential Assistant Helps Precise Magnetic Assisted Vascular Anastomosis | ||
| Application Of Computer Intelligent Surgical Confidential Assistant In Laparoscopic Liver Resection | ||
| Prospects for intelligent surgical machine assistants in precision liver segment resection | |
| Intraoperative Image Detection and Clearing System Based on Generative Adversarial Network | |
| Application of Orthogonal Decomposition in Surgical Image | |
| Segmentation-for Unsupervised Adaptability in Intraoperative Surgical Image Recognition Navigation | |
| 微创术中影像语义分割模型训练方法及装置 | |
| 微创术中影像语义分割模型训练方法、语义分割方法及装置 | |
| 一种图像语义分割方法 | |
| 基于深度学习的图像语义分割方法 | |
| 图像语义分割模型训练方法、图像语义分割方法及装置 | |
| 图像语义分割方法和语义分割装置 | |
| 微创手术中影像区域识别模型的培训方法、区域提取方法及设备 | |
| 微创术中影像语义分割模型的训练方法和分割方法 | |
| 一种微创术中影像语义分割方法和影像语义分割装置 | |
| 伦理审查 | 西安交通大学第一附属医院伦理委员会审查批件 |
| 西安交通大学医学部医学生物科研伦理审批件 | |
| 社会评价 | 郑南宁院士项目推荐函 |
| 赵玉沛院士项目推荐函 | |
| 彭淑牖院士项目推荐函 | |
| 中国教育报专访 | |
| “思源”医疗器械高峰论坛邀请函 |
1. 发表论文
37.3Q1
Enhancement of TKI sensitivity in lung adenocarcinoma through m6A-dependent translational repression ofWnt signaling by circ-FBXW7
Abstract
Background:Tyrosine kinaseinhibitors(TKls)that specificalytarget mutational points in the EGFR genehave significantly reduced sufferingand provided greaterrelieftopatients with lung inclinical treatments toovercomeresistance to both original andacquired T79oMand L858R mutational points.Nevertheless,theissue of treatment failureresponse has emergedasan insurmountable problem.
Methods:Byemployingacombinationofmultipleand integratedapproaches,wesuccesfuly identifiedadistinct population within the tumor groupthat playsa significant rolein carcinogenesis, therenewaland repopulation of stem-likecels.To investigate the underlying mechanisms,we conducted RNAMicroarrayandm6AEpi-TranscriptomicMicroarrayanalyses,followedbyassessment oftranscriptionfactors.Additionally,wespecificallydesignedatagtodetectthepolypeptidecircRNA-AA,and its expression wasconfirmed through m6A regulations.
Stemsignatures associating SOX2 antibody helps to define diagnosis and prognosis prediction with esophageal cancer
PMID:35382656 PMCID:PMC9004505 DOI:10.1080/07853890.2022.2056239
Abstract
Background:esophageal cancer isone of thedeadliestdiseasesworldwide.Due to the ineffectual screening methods referring to earlydiagnosis,most people have lost their chance of radical resectionwhen diagnosed with esophageal cancer.Thisaimof this study wasdesigned toevaluate thelatentvaluesofthestemsignatures-associatedautoantibodies(AABS)inpredicting theearly diagnosis,and particularly seeking the precise predictive outcomes with sensitive SOX2.We also studiedthepotentialimmunotherapeutictargetsand prospectivelong-termprognosispredicatorsof esophageal cancer.
稿件录用通知
尊敬的彭子洋作者：
您好！贵稿件“增强现实、虚拟现实与混合现实在腔镜肝脏外科中的应用进展”，稿件编号：119322-20230831-00104，作者：彭子洋、王志博、巴赫、颜彦、彭浩茜、李宇、刘学民、向俊西、吴荣谦、吕毅，已通过审核，符合我刊发表要求，予以录用。特此通知。
《中华肝脏外科手术学电子杂志》是经国家新闻出版广电总局批准登记，由国家卫生健康委员会主管，中华医学会主办，中山大学附属第三医院承办的优秀学术期刊。国内统一刊号：CN11-9322/R，国际连续出版号：ISSN 2095-3232。
稿件录用通知
尊敬的彭子洋作者：
您好！贵稿件“智能化辅助图像实时去雾技术在腹腔镜胆囊切除术中应用研究”，稿件编号：119322-20230831-00103，作者：彭子洋、王志博、王丹、彭浩茜、王蕾、彭薇、王娟娟、李宇、刘学民、吴荣谦、向俊西、吕毅，已通过审核，符合我刊发表要求，予以录用。特此通知。
《中华肝脏外科手术学电子杂志》是经国家新闻出版广电总局批准登记，由国家卫生健康委员会主管，中华医学会主办，中山大学附属第三医院承办的优秀学术期刊。国内统一刊号：CN11-9322/R，国际连续出版号：ISSN2095-3232。
CMAIC
2023中国医学人工智能大会
China Medical Artificial Intelligence Conference
Intelligentdigitalfoggingtechnologyshowsgreat potential inlaparoscopichepatectomy surg
Affiliation:Schoolof Future Technology,National Local Joint Engineering Research CenterforPrecisionSurgery&RegenerativeMedicine,Shaanxi Provincial Center forRegenerative Medicineand Surgical EngineeringXian Jiaotong University,Xi'anCity，ShaanxiProvince,71oo61.China ZiyangPeng,ZhiboWang,YongtaiMa,Dan Wang,Shuyan LiuFeng Ma,YuLi,XueminLiu,RongqianWu,Yi Lv
Keywords
Laparoscopichepatectomy;digital surgery intelligent intraoperative defogging:precisionsurgery; surgical safety
Background and Aims
Aseriesof smoke,aerosols,and other gases generated during surgical hemostasisoperations such aselectro-knife and ultrasonicknife inlaparoscopic surgery can interfere with the normal courseof the surgical procedure.Inthis study,weaimed toutilizeartificialintelligene algorithmstoassist intraoperative defogging toreduce interference withthe operator'sfield of view during surgical operations.
Original mageofLaparoscopk Sugary
METHODS
Arelevant dataset wascreated from videos of smoke in the operating areaduring surgery, and several algorithms were evaluated usingdeep-sea experimentsto assessthe effectiveness and speed of each type ofalgorithm.
RESULTS
Theintraoperative smokepersistence timeof 53patients with laparoscopic hemihepatectomyranged from35to78 min,witha mediantime of59min;the mirrorwasremoved and wiped 23to44 timesduring the operation,witha median numberof times of 35;and an averageof 30swasspent on each occasion.Intelligent-assisted image defogging time averaged 0.01s,and the successrate of image processing was 93%(163680/176000).Intelligentassistedimagedefoggingwaseffectivein reducing intraoperativemirror-wiping time（Z=-3.145,P<0.05).
lagAelDgi
CONCLUSION
Ourself-designed yun-transform algorithmshowed excellent effectiveness inautomatingdigitalthrough-fog elimination effectivelyperforming fog identificationand removal after grading, significantly improving intraoperative smoke interference withthe operator's field ofview.
CMAIC
2023中国医学人工智能大会
China Medical Artificial Intelligence Conference
Intelligent Surgery Enters the Blind Spot of Lumpectomy Liver Resection
hoolofFuture Technology,National LocalJoint Engineering esearch sionSurgery&RegenerativeMedicine,ShaanxiProvir Regenerative MedicineandSurgical EngineeringXianJiaotong University,XianCity Shaanxi Province,710061.China
Authors:Ziyang Peng,Zhibo Wang,Yan Yan,He Ba,Haoqian Peng，YuLiGuan Yu,Feng Ma,Xuemin Liu，Rongqian Wu,Yi Lv
Keywords
lumpectomyhemihepatectomy; intelligent intraoperative recognition;total surgical coverage;intraoperativevascular alert;personalized precision surgery
Background and Purpose
Moreaccurate identificationof anatomical structuresand surgical processes isoften required for planning when performinga lumpectomy of theright halfof the liver.Inthis studywe optimized our process in laparoscopic hepatectomyby designing an intelligent surgical screenrecognition software module.
Results
Anintelligent surgicalmodulecan effectivelyperformreal-time alignment of3-Dreconstruction and surgical screen during surgery byintroducinga 3Dreconstruction model before surgery,clarifying anatomical structures,suggesting surgical stagesandalerting potential deep livervessel locations,and providing early warning of danger zones,which effectivelyreduces intraoperative complicationsand optimizes surgical processes.Itcan reduce vascularandnerve injuriesby 28.23%and shorten the operation timeby16.54%.
Methods
Adatatraining set was established by multicenterdataof 53 casesof laparoscopicrighthemihepatectomy, andanadditional21casesof laparoscopicright hemihepatectomy surgical videoswere used asan extemal validationsettoclarify the intraoperativeorganboundariesand to dothe surgical warnings,and the algorithmswereevaluated bythe surgical real-timevideorecordings videosandthereal-time screen of the laparoscopicsimulator.
Figure1Matched images betweer eintelligent
Conclusion
Intelligent surgical software can be effectivelyused inlumpectomy for righthemihepatectomytoprovide more accurate and safe surgical adviceto the operatorand bring better survival benefitstothepatients. Laparoscopichemihepatectomy; intelligent intraoperativerecognition; totalsurgical coverage;intraoperative vascularalert;personalized precision surgery.
CLINICAL CONGRESS 2023
OCTOBER22-25/BOSTON,MA
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Abstract:Application OfComputer Intelligent Surgical Confidential Assistant In Laparoscopic Liver Resection
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Session: SP302-2|e-Posters Vl-Station 2: Hepatobiliary and Pancreas Il
DateandTime:10/25/20232:30:00PM-10/25/20234:00:00PMEasternTime
Authors:ZiYang Peng,MD,PhD,Zhibo Wang,PhD,Juanjuan Wang,PhDandYi Lyu,MBBS, MS,FACS.TheFirstAffiliatedHospital ofXianJiaotongUniversity，Xi'an,China,TheFirst Affiliated HospitalofXi'anJiaotong University，Xi'anCity，ShaaniProvince,Cinese mainland,China,TheFirstAffiliatedHospitalofXi'anJiaotongUniversityXi'an,China
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CLINICAL CONGRESS 2023
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Dear ZiYang Peng:
Onbehalf of the American College of Surgeons'Scientific Forum Committee,the Division of Education ispleased toinviteyou topresentyour acceptedabstractat Clinical Congress2023.
Abstract: Intelligent Surgical Confidential Assistant Helps Precise Magnetic Assisted Vascular Anastomosis
Authors:ZiYang Peng,MD,PhD,Zhibo Wang,PhD,Juanjuan Wang,PhDandYi Lyu,MBB, MS,FACS.TheFirstAffiliated HospitalofXi'anJiaotong University，Xian,China,TheFirst AffiliatedHospitalofiaiotongUivesity，ianCityairovince,Cine mainland,Cina,TheFirstAfiliatedHospitalofi'anJiaotongUniversity，Xi'anCina
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【论文录用通知】2023中国生物医学工程大会暨创新医疗峰会
中国生物医学工程学会2023-03-2601：31
发至我自己的邮箱
（此邮件由de0465b6-cb32-11ed-aff1-525400e00ae3@medcon.org.cn代发）
2023中国生物医学工程大会暨创新医疗峰会
论文录用通知
尊敬的彭子洋老师：
由中国生物医学工程学会主办的“2023中国生物医学工程大会暨创新医疗峰会(BME2023）”将于2023年5月18-21日在苏州金鸡湖国际会议中心召开。
BME2023荣幸地邀请到诺贝尔奖获得者ThomasC.Sudhof教授和StefanW.Hell教授，拉斯克医学奖获得者卢煜明教授，以及其他国内外著名学者为大会做主旨报告。同时将为工程师、临床医生、科学家、企业家以及青年学者和学生提供多方位的学术交流平台，根据专业分会方向设置的分会场、举办论坛、以及壁报交流等。除学术交流，还将举办各类展示，包括医疗器械公司、中小企业、初创企业、生物医学工程项目、研究所和大学等。诚挚的邀请您参加此盛会。
经大会专家审稿，您投的稿件评审结果如下：
| ID | 稿件题目 | 第一作者 | 评审结果 |
| 2859 | 基于精准医疗下肺癌类器官模型的研究 | 彭子洋 | 纸质壁报 |
| 所有作者：彭子洋，吕毅，任宏 | |||
| 3526 | Research on lung cancer organoid model based on precision medicine | 彭子洋 | 纸质壁报 |
| 所有作者：彭子洋，hong ren，lv yi | |||
您也可以登入大会网站（www.csbmemeeting.org）个人后台查询评审结果。
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FIS2023中国普外科焦点问题学术论坛 聚焦前沿规范提高创新发展合作共赢
PO 017
Prospects for intelligent surgical machine assistants in precision liver segment resection
Affiliation：School of Future Technology,National LocalJoint Engineering Research Center forPrecision Surgery&Regenerative Medicine,ShaanxiProvincial CenterforRegenerative Medicineand Surgical
Objective
Laparoscopic precision liver segment resectionoftenrequiresaccurate identification ofanatomical structuresand surgical proceduresplannedby haemodynamic zoning.In this studywe designedan intelligentsurgicalaid to optimize our laparoscopic liversegment resection procedureandavoid surgical complications,providingamore precise andstandardized surgical treatment system forpatients.
Results
The intelligent surgical machine assistantwasable torapidlyimportthe reconstructed3Dmodel ofthe liver preoperatively，matchthe3Dpicture withthereal-time surgical picture during surgery,clarifytheanatomical structureand vascular distribution of liversubsections,assessthe surgical stageandthe surgical instrumentsto be usednext,alert the surgical path with keyframes,andprovideearlywarning ofdangerousareas,effectivelyreducing intraoperativecomplications.The potential for lymph nodedetectionwas improvedby37.21%，vascularnerve injurywasreducedby17.16%,operative timewaseffectivelyreducedby18.95%， postoperative patient length of staywas reducedby15.29%，and97%ofpatients reported satisfaction.
Methods
Webuilt ourdatasetusinga largeamount of international multicenter video data of laparoscopic liverresection.The effectivenessand speed of various convolutional neural networkalgorithms wereevaluated throughdeep-sea experiments.Thealgorithmswere introducedpreoperativelythrougha3D reconstructionmodel,and the effectivenessand safetywereassessed through livevideo of thesurgeryand live images froma laparoscopicsimulator.
Figurel Image segmentation pattern diagram
Conclusion
The Intelligent Surgical Machine Assistant canbesafelyandeffectivelyused for laparoscopic hepaticsegmental resection, significantly reducing the chanceof surgical complications，providingoperators withmoreaccurateand safer surgical recommendations,and providingpatients withabetterpostoperativerecovery outcome and experience.
PO 018
Intraoperative Image Detection and Clearing System Based on Generative Adversarial Network
Affiliation：School of Future Technology,NationalLocal Joint EngineeringResearch Centerfor Precision
Objective
During laparoscopicsurgery,the thermal decomposition of human tissue generates smokeandaerosols,whichcan interfere with the surgical process.Inthis study，we aimedtodevelopagenerativeadversarial network(GAN)model todetectand clear intraoperativesmoke,inordertooptimize thesurgical processand providebetter medical services forpatients.
Results
The deep learningalgorithmcan effectivelyqualifyandquantifythefog generatedbythe image,whichisdivided into five levels:smoke thatdoesnotaffect surgery,local smoke thatcausesthe surgical operationareatobeblurry，some smoke that causes humanorgan recognitiontobeblurry，a largeamount ofsmokethat cannot identify the boundaries ofvarious tissueorgans,anda largeamount ofsmoke thatcompletely blocksthe surgical field of view.The optimizedalgorithmcan identifysmoke and clearfogin 0.01seconds.The comprehensivedefoggingrateinthe surgicalareawas91.71%,thevascular injuryratewasreducedby43.26%，and the operative timewas effectivelyreducedby 15.34%.
Methods
We established a related dataset by collecting smoke videos produced during different stages of various surgeries from multiple international datasets,as well asa largenumber of surgical videos from our center,whichwere segmented frameby frame.Wethenevaluated the effectiveness and speedofvariousalgorithmsthrough ablationexperiments,using intraoperative smokeinreal-timevideosandendoscope simulator images.
Figure1 Artificial intelligence automatically endoscopic surgery
Conclusion
Thedeep learningimagedefogging algorithmcaneffectively identifyand remove classified fogduring laparoscopic cholecystectomy，significantlyreducing the interference of smokeon the surgeon's visualfield.
FIS2023中国普外科焦点问题学术论坛 聚焦前沿规范提高创新发展合作共赢
PO019
Application of Orthogonal Decomposition in Surgical Image Segmentation-for Unsupervised Adaptability in Intraoperative Surgical Image Recognition Navigation
Authors:ZiyangPeng，Zhibo Wang,Yu Li,Xuemin Liu,Lyu Yi
Objective
Deep learning-based medical imagesegmentation methods havemadesignificantprogress.However, existing data processing methodsare sensitive to imagedistribution,so slight changesin surgical imagescan leadtoadecreaseinimage recognition performance.Toaddress this problem, thisstudy proposesanew orthogonal decompositionadversarialdomainadaptation architecture formedical imagesegmentation.
Results
Intheslightlyadjusted laparoscopicsurgical images,ouralgorithmwas fullyvalidatedand couldeffectively identify organsand surrounding bloodvesselsand nerves,withadetection rate 37.21%higher than that of the humaneye.Inthe process of large-scale laparoscopicimagechanges, ouralgorithmcouldalsoeffectively identifythe relativepositionsof differentorgans,increasing doctor satisfactionby47.21%andpreventing adversecomplicationscausedbysurgical errors.
Methods
Comprehensive experimentswere conducted on multiple international publicdatasetsanda largeamountof intraoperative surgical video content, including laparoscopic cholecystectomy segmentationdataset, laparoscopic livercancerresection surgery segmentationdataset,and laparoscopic gastriccancer resection surgerysegmentationdataset. Ablationexperimentswere conducted tovalidate the effectiveness of the relevantalgorithms.
Conclusion
The field of surgical image recognition and segmentationdeserves further indepth research and exploration.In this study,we reconsidered the problem of imagerecognitioncaused by intraoperative surgical image adjustmentsand proposed anew algorithmicframeworkfor intraoperativesurgical image recognitionand segmentation.This framework helpstoclarifytherelative positions of the surgical site and surroundingbloodvesselsandnerves, effectivelyreducing the incidenceof surgical complicationsand widely recognized bysurgical doctors.
2. 伦理委员会审查批件
西安交通大学医学院第一附属医院
伦理委员会审查批件
| 项目名称 | 智能外科机要助理在肝胆胰微创手术中的应用研究 | |||||
| 项目来源 | 陕西省科技厅 | |||||
| 牵头单位 | 西安交通大学第一附属医院 | |||||
| 承担科室 | 肝胆外科 | 项目负责人 | 刘学民 | 职称 | 主任医师 | |
| 审查方式 | ■快速审查 □会议审查 | |||||
| 送审材料 | 1.伦理审查申请表2.研究方案 版本号：V1.0 版本日期：2020年7月12日3.知情同意书 版本号：V1.0 版本日期：2021年3月2日 | |||||
| 审查结论 | 同意 | 作必要修正后同意 | 作必要修正后重审 | 不同意 | 终止或暂停 | |
| √ | ||||||
| 伦理会意见： 1.经审查，本项目研究方案未违背伦理原则，同意开展本研究。 2.该研究进行过程中，伦理委员会进行定期跟踪审查，审查频率：3个月□ 6个月□ 12个月■ 主任委员： 范元坤 时间：2021年5月24日 | ||||||
| 注意 | 1.对已批准的临床研究方案、知情同意书等材料的任何修改及主要研究者更换等，请及时通知本伦理委员会重新审查，获得批准后执行。 2.根据本伦理委员会的定期跟踪审查频率，请在审查日到期前一个月提交定期跟踪审查报告。 3.发生严重不良事件及时报告。暂停/提前终止临床研究或项目结束，请提交相应的报告。 4.本审查结果只涉及对伦理问题的审查结论，如相关研究要求办理相应手续，如到上级部门办理审批/备案手续，或按医院要求需要签署合同书/协议书的，请在项目开展前先行办理上述手续。 | |||||
本伦理委员会严格遵循ICH-GCP、GCP和相关法规的要求进行构建、运作、实施各项操作程
序。联系地址：西安市雁塔西路277号 联系人：张彩霞，电话/传真：85323473
西安交通大学医学部医学生物科研伦理审批件
3. 社会评价
项目推荐函
本人对于彭子洋同学创立的陕西云链智康科技团队开发的智能外科机要助理系统总体评价如下：
该系统稳定性强，操作简便，易于掌握，将其应用于医患沟通，明显提高了医患沟通的效率。该系统为术后治疗方案的制定提供了准确的参考价值。通过互联网+技术完成的远程会诊系统APP,实现了术后患者的家庭环境康复，实现了手术医生-患者-随访医生的有效沟通，节约了医疗资源、降低了医疗费用。
总之，该系统智能、高效，不但可大大减轻医护人员的工作负担，而且有助于提高手术记录质量，并将助力互联网+时代智能医疗体系的建立，将带来外科手术记录的一场革命，整体达到国际领先水平。
项目推荐函
本人对于彭子洋同学创立的陕西云链智康科技团队开发的智能外科机要助理系统总体评价如下：
第一：该项目解决了目前外科手术记录“千术一式”的问题，可以实现外科手术记录的个体化及精准化，是实现“精准医疗”的必备条件。
第二：该项目临床应用范围广泛，包括：手术图文记录，医患沟通，外科年轻医生教学，术后进一步治疗的方案确定，远程会诊等。
第三：该项目可自成体系，亦可与医院信息系统结合，实现真正的“数字化医疗”，方便手术记录的保存及调取工
第四：该项目方便学习，设备简练，易于在医院推广应用。特此推荐。
项目推荐函
彭子洋同学创立的陕西云链智康科技团队自主研发的智能外科机要助理系统具有良好的工作性能，适用于多种医疗环境，提高手术记录的准确性，并可大大节省医疗资源，并利用该系统进行外科教学及医患沟通，取得良好的效果。该系统在很短时间内就在陕西省内外多家医院进行了推广应用，说明了该系统具有很强的推广应用前景，且易于被外科医生、手术患者所接受，特此郑重推荐。
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■着力推进卓越工程师培养
“对新能源处理特性进行分析和提取，进而进行电力电量平衡的测算，可以更好地指导国家电力系统的运行和调度。这项研究的数据量庞大，如果没有企业导师指导，我很难将自己在学校所学运用到实际当中。”对西安交通大学2022级储能方向博士生胡骞文来说，到南方电网公司实习并得到企业导师指导是段重要的成长经历。同时，依托课题组与广东电网公司的校企合作项目，胡骞文关于“多类型规模化的储能智能规划与接入技术研究”的博士课题研究也正在展开。
像胡骞文一样，在西安交大，越来越多的工程硕博士生在学校和企业双导师的指导下，将企业攻关课题作为自己的研究课题。近年来，该校积极适应新一轮科技革命和产业变革新趋势，把卓越工程师教育培养作为“双一流”建设的重要任务，持续深化工程教育改革，努力培养造就爱党报国、敬业奉献、具有突出技术创新能力、善于解决复杂工程问题的卓越工程师队伍，取得阶段性成效。
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“思源"医疗器械高峰论坛邀请函
“思源"医疗器械高峰论坛
暨西安交通大学医电校友会第十四届校友论坛
融合创新协同发展
会议简介
一年一度的"思源“医疗器械高峰论坛暨西安交通大学医电校友会第十四届校友论坛即将于5月16日如期举行。“思源"医疗器械高峰论坛，作为每年CMEF期间的传统活动，至今已举办十三届，累计参与学者、企业家、交大师生万余人次，并受到与会人员的广泛好评。回顾去年论坛，受疫情影响，原计划在上海举办的会场转场至深圳，随后又转向线上举办。校友们在云端相聚，共同交流与探讨医疗行业的发展现状与未来趋势。虽然线上论坛取得了良好的关注度和成功，但我们更期待着疫情结束后的线下相聚，面对面交流和分享后疫情时代医疗器械行业的最新成果和发展动态。
本次论坛的主题依旧是”融合创新，协同发展”，旨在探讨医疗器械行业的新技术、新政策、新需求，分享行业的最新发展动态。论坛邀请的专家覆盖教育、企业、投资等多个领域，希望通过本次论坛，促进不同领域专家、学者、以及校友们之间的交流和合作，为医疗器械行业的发展提供更多思路和方向。我们相信本次论坛将为参与者带来难得的交流机会和宝贵的经验启示，同时也为医疗器械行业的创新与协同发展注入新的动力和活力。
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圆桌论坛：新形势下促进医工融合创新，协同发展
谈庆辰德资本创始合伙人
郑毅瑞莱谱（杭州）医疗科技有限公司创始人
骆志坚 聚融医疗科技（杭州）有限公司CEO
李龙 西安交通大学生命学院教师西安穹顶医疗科技有限公司创始人兼CEO
彭子洋 西安交通大学未来技术学院医工学方向临床医学博士