5G技术在创伤急救中的应用及探索

随着数字科技的快速发展,以往的创伤医疗救治模式已不能适应未来医学现代化的发展要求。大数据共享背景下的5G技术助力创伤急救成为人们关注的重点,并已在一些救治过程中尝试应用。探索出更多5G技术应用于创伤识别、救治及预防的新场景,有望实现区域急救资源合理调配、生命体征信息实时传输及创伤救治各个环节的有效衔接,提高创伤患者的救治效果。     

区域性创伤救治体系中郑州市“1小时创伤急救圈”模式初步探索

建设高效的区域性创伤救治体系是降低创伤患者病死率和致残率的重要措施。我国目前创伤救治工作中存在专科救治能力欠缺、医疗资源分配不均等问题,导致院前救治能力不足。为此,笔者以郑州中心医院创伤中心为核心救治单位,从空间上构建“1小时创伤急救圈”,并结合所取得的初步成就,以此为模式开展创伤救治工作。笔者从建立郑州市“1小时创伤...     

5G网络医联体联动模式在严重创伤救治中的应用效果

严重创伤是45岁以下青壮年主要的死亡原因。基层医院严重创伤救治经验的匮乏、医联体间缺乏快速有效的转诊模式均导致严重创伤的致残率及病死率增加。而5G网络信息高速率、低延时实现多学科会诊前置基层医院、诊疗早期共同决策, 弥补了基层医院救治经验的不足。医联体间依托信息联动的精简诊疗流程, 可缩短创伤急救时间。为此, 笔者采用回顾性队列研究分析2018年1月至2022年10月台州恩泽医疗中心(集团)恩泽医院收治的128例基层医院转诊严重创伤患者临床资料, 初步分析5G网络医联体联动救治模式在严重创伤救治过程中的应用效果。     

急诊创伤患者临床数据特征分析

急诊创伤患者具有病情复杂、高病死率和高致残率的特点,创伤患者在急诊救治过程中,会产生大量有价值的动态数据,随着医疗单位信息化体系建设的不断完善和大数据、人工智能等一批新技术的出现,为基于创伤数据的临床基础与应用研究提供技术支撑[1].本文回顾性分析2019 年1月—2020年12月解放军总医院第一医学中心急诊门诊诊断包含"伤"或"骨折"等创伤患者的临床资料,从性别、年龄、分诊科室分布、就诊人数季节性变化以及抢救间的患者数据进行统计分析,结合创伤患者的救治路径分析创伤数据临床特点及数据分析的可行性,为致伤机制研究及伤情快速诊断、个性化治疗相关研究提供参考.     

我国颅脑创伤救治现状与展望

经过临床医护工作者和基础研究人员的不懈努力,我国颅脑创伤患者救治水平有了长足提高,死残率明显降低.但是,必须清醒地认识到,我国重型颅脑损伤患者的救治效果与国际先进水平仍有较大差距.在颅脑创伤患者现场抢救与转运、急诊室抢救和处理、手术指征和方案、术后监护和护理、脑水肿防治和颅内高压处理、并发症防治和康复措施等诸多环节尚存在不足,缺乏规范化和科学性.为了进一步提高我国颅脑创伤患者救治效果,目前必须切实重视以下五个方面的临床工作.     

创伤外科病历书写存在的问题及改进建议

创伤外科的病历书写与传统外科存在区别。创伤外科病历记录了创伤救治的医疗全过程, 反映创伤技术水平及救治效果。目前, 创伤外科病历书写大多沿用传统外科各专业的书写模式, 不能反映创伤患者整体损伤情况及诊断治疗经过, 导致创伤外科病历质量参差不齐, 妨碍创伤救治与学术交流。创伤评估有其特殊性, 传统体格检查方法并不完全适用于创伤患者。此外, 随着诊疗技术的进步, 临床评估过程中也可以避免采用一些导致疼痛、延误抢救, 甚至造成损伤的查体方法和诊疗操作。随着创伤救治理论技术和学科发展, 创伤外科病历的书写有待进一步规范和统一。笔者通过分析创伤外科病历书写的特殊性, 梳理和总结目前病历书写中存在的问题并提出改进建议, 为规范创伤外科病历书写、提升创伤救治综合实力提供参考。     

超声在创伤救治中的应用进展

超声在创伤救治领域得到广泛应用,已成为快速评估病情的重要手段。近年来随着超声技术的不断进步,其在创伤救治中的应用内容更加丰富,除了经典的创伤超声检查(FAST),还扩展到肺、心脏、颅脑、眼、肌肉骨骼、胃肠、血管超声及超声造影技术等,可对创伤患者进行从头到脚的评估和引导治疗。超声在创伤救治的不同阶段、不同环境下均有重要价值,本文介绍了相关的应用。     

外军创伤系统建设对我军的启示

外军在批量伤员救治的军事实践中建立了创伤系统,用于伤亡态势实时感知、医疗决策和战救质量改进等,并尝试在军民融合工作机制下实现创伤的预防,院前、院内救治,康复及教育等功能.我国在严重创伤批量伤员救治具有举国体制优势,而民用创伤系统还处于发展阶段,建立一个可用于军事行动的创伤系统并实现军民融合式应用还任重道远.本文结合笔者研究和救援实践体会,借鉴外军创伤系统的经验,总结了外军创伤救治系统的经验和不足,提出相关启示参考.     

积极开展循证医学研究,提高中国颅脑创伤患者的救治水平

2010年世界范围内大宗病例统计表明,重型颅脑损伤患者的死亡率仍然维持在30％左右,致残率也很高[1].2011年中国颅脑创伤资料库显示,47所医院重型颅脑创伤患者死亡率为21.83％.加强重型颅脑创伤临床救治技术的临床应用研究、提高重型颅脑创伤患者救治效果仍然十分迫切.当前,我们应该重点做好以下几方面工作.     

腹腔镜在腹部创伤中的应用

腹部创伤的诊治手段正在不断地更新发展,微创技术的运用为腹部创伤的诊治带来了革命性的变化.随着微创外科技术的不断进步,诊断性和治疗性腹腔镜在腹部创伤救治中的作用日益重要.本文从腹腔镜在腹部创伤救治中应用现状、应用价值、适应证、禁忌证、相关并发症及诊治腹部创伤面临的挑战等方面进行探讨,指出严格筛选腹部创伤患者使用腹腔镜技术...     

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.     

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.     

网络版创伤数据库系统V3.0的研究

目的 研制适用于医院创伤救治信息积累和分析的创伤数据库及其网络软件系统.方法 针对创伤救治过程与创伤救治基本信息,基于windows操作系统和SQL SERVER 2000数据库平台,采用vb.net和asp.net软件编程语言,以B/S访问模式进行开发.结果 建立了适应于医院管理和创伤救治流程的创伤数据库结构与内容,形成了规范的数据库字段313个;编制出网络版创伤数据库系统V3.0软件.建立了创伤救治信息流,实现了创伤ICD诊断与简明损伤定级(AIS)的关联选择、创伤评分与救治质量的评估、对数据库任意字段内容进行查询并形成包括三重交叉表在内的高级报表等功能.该系统在多家医院和"5·12"地震伤员资料调查中得到很好应用.结论 研制出适用于医院创伤救治信息登记的创伤数据库内容和网络版软件系统,使医师能规范地录入收集创伤发生、救治、结局与质量评估等的信息,并能很方便地进行资料的查询和分析.     

加快创伤与急诊外科建设步伐

建设创伤与急诊外科已经成为当前众多医院创新发展的选择之一。创伤与急诊外科的建设是基于患者救治的需要,应能提供满足患者紧急救治需要的最优医疗机制,能在黄金时间内实施创伤确定性处理和多发伤的跨学科整体救治。我国创伤与急诊外科建设的理论基础和学术平台已经具备,不是各级医院的法定必建学科及没有所属独有专业是影响其建设的最主要因素。三甲医院中21.03%已经设置创伤与急诊外科或类似学科,但运行模式仍待规范,"创伤外科+创伤ICU+急腹症外科"模式最具潜力。     

院前创伤急救进展：新研究带来的机遇与挑战

院前创伤急救是整个创伤救治体系的重要组成部分。大多数院前急救医疗服务（EMS）体系源自于军事医学经验和改进,对于创伤患者的确定性急救自现场就应尽快开始。院前创伤急救成功的关键在＂创伤急救ABC＂：即气道、呼吸、循环,核心是建立并保证气道、通气、液体复苏和快速转运至就近的恰当医院。传统的院前处理面临着新的研究成果的挑战,同时新技术、新方法、新工具的应用也使院前创伤急救变得更加容易,创伤患者的救治结局也得以改善。     

中国创伤外科发展现状与展望

近30年来,中国创伤外科发展取得了斐然成绩。在北京、重庆、武汉、杭州等创伤中心的先期引领示范下,创伤救治学科建设蓬勃发展,创伤救治体系的"中国模式"初步建立;构建了创伤外科的理论基础及学术交流平台;总结了创伤急救的基本理论与技术和创伤外科关键理论与核心技术;创伤救治能力,特别是对严重多发伤的救治能力显著提高。但是创伤救治体系尚不完善,总体创伤救治水平仍落后于发达国家。为实现《"健康中国2030"规划纲要》,到2030年人均预期寿命再增3岁达到79岁,创伤外科发展面临重大历史机遇与挑战。     

严重创伤院前急救中值得探讨的几个问题

院前急救是严重创伤救治链中非常重要的一环,也是创伤救治体系"三环理论"中的一个基本环节,是我国急救医疗服务体系中的重要组成部分,是提高严重创伤救治成功率、降低死亡率的根本保证。目前国内院前急救的模式还不统一,存在较多争议,但创伤院前急救的"快、准、稳"是院前急救工作者永远追求的目标。通过回顾文献、结合临床工作经历,就创伤院前急救中涉及的几个问题做一初步探讨,供同仁参考。     

Artificial intelligence in breast ultrasound

Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.     

Strengths,Weaknesses, Opportunities,and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since the end of the last century, several ML algorithms have been introduced for a wide range of common imaging tasks, not only for diagnostic purposes but also for image acquisition and postprocessing. AI is now recognized to be a driving initiative in every aspect of radiology. There is growing evidence of the advantages of AI in radiology creating seamless imaging workflows for radiologists or even replacing radiologists. Most of the current AI methods have some internal and external disadvantages that are impeding their ultimate implementation in the clinical arena. As such, AI can be considered a portion of a business trying to be introduced in the health care market. For this reason, this review analyzes the current status of AI, and specifically ML, applied to radiology from the scope of strengths, weaknesses, opportunities, and threats (SWOT) analysis.