消化内镜人工智能：使命促进跨界合作研究

提高消化内镜技术水平是各医院、学术组织和政府职能部门等多年努力践行的工作,目前存在的一些问题,需要借助人工智能的帮助。基于深度学习的卷积神经网络已广泛应用于各种消化内镜人工智能辅助系统。《中华消化杂志》本期人工智能专题刊载的消化内镜人工智能辅助系统与设备可从部位质量控制、隆起型病变检出和息肉性质鉴别3个层面辅助消化内镜检出和甄别病变;减轻胶囊内镜医师阅片的工作负担,以便聚焦小肠出血病灶的检出。这些医工结合的研究,敦促消化病学与人工智能技术的深度融合。     

人工智能在消化内镜中的应用进展

随着人工智能技术的不断发展,其在医疗领域的应用越来越广泛,包括疾病诊断、治疗方案选择、判断预后等多方面。本文就人工智能技术在消化内镜领域中的应用,围绕辅助胃镜检查、结肠镜检查和胶囊内镜检查三个方面进行了详细介绍。     

消化内镜人工智能的现状及展望

人工智能正成为各国争相发展的重点科技领域，近年来人工智能在医学领域发展迅速。现就人工智能的进展历程进行概述，介绍消化内镜领域人工智能的应用现状及展望其未来方向。     

人工智能在消化内镜质量控制中的 应用与发展

随着人工智能在各领域的应用,消化内镜质量控制能否踏着这个科技浪潮进行一次全新的变革,智能化的质控管理方式如何在消化内镜领域提高质控效率及质量越来越受到关注。本文从消化内镜质量控制的现状和面临的问题出发,着重探讨了人工智能在消化内镜质量控制方面的应用现状和发展前景,为消化内镜质量控制的智能化发展提供了一种现实可行的构想。     

中国消化内镜的过去、现状与展望

伴随着全球科技进步的步伐,现代医学科学技术的发展日新月异。进入21世纪后,微创医学成为当今医学的主流和趋势,在微创医学的核心技术中,以现代消化内镜技术发展最为全面和成熟,已经成为消化系统疾病诊断、治疗最重要的手段之一。在中国消化内镜事业的发展历程中,很多老前     

基于人工智能的自动内镜下病灶尺寸测量系统（含视频）

目的　开发一个基于人工智能的自动内镜下病灶尺寸测量系统,并测试其实时测量白光内镜下病灶尺寸的能力。方法　测量系统由3个模型组成：首先由模型1识别视频的连续图片中有无活检钳,有钳者标记钳叶轮廓;随后由模型2对有钳图片进行分类,分为张钳图片和未张钳图片;与此同时,模型3识别视频的连续图片中有无病灶,有病灶者标记边界;最后系统根据活检钳钳叶轮廓与病灶边界的像素对比,实时计算出病灶尺寸。数据集1由回顾性收集的武汉大学人民医院2017年1月1日—2019年11月30日4 835张图片组成,用于模型的训练和验证;数据集2由前瞻性收集的武汉大学人民医院内镜中心2019年12月1日—2020年6月4日检查拍摄的图片组成,用于测试模型分割活检钳边界和病灶边界的能力;数据集3由151个模拟病灶的302张图片组成,每个模拟病灶包括活检钳倾斜角度较大（与病灶垂直线夹角45°）和倾斜角度较小（与病灶垂直线夹角10°）情况下的图片各1张,用于测试模型在活检钳不同状态下测量病灶尺寸的能力;数据集4为视频测试集,由前瞻性收集的武汉大学人民医院内镜中心2019年8月5日—2020年9月4日检查拍摄的视频组成。以内镜医师复核后结果或内镜手术病理作为金标准,观察模型1识别有无活检钳的准确率、模型2分类活检钳状态（张钳或未张钳）的准确率和模型3识别有无病灶的准确率,用交并比（intersection over union,IoU）评价模型1的活检钳钳叶分割效果和模型3的病灶分割效果,用绝对误差和相对误差评价系统的病灶尺寸测量能力。结果　（1）数据集2共纳入1 252张图片,有钳图片821张（其中张钳图片401张、未张钳图片420张）、无钳图片431张;包含病灶图片640张、不包含病灶图片612张。模型1判断无钳图片433张（430张准确）、有钳图片819张（818张准确）,识别有无活检钳的准确率为99.68%（1 248/1 252）,以818张模型1准确判断有钳图片的数据统计模型1的活检钳钳叶分割效果,平均IoU为0.91（95%CI：0.90～0.92）。使用模型1准确判断的818张有钳图片评价模型2的活检钳状态分类准确率,模型2判断张钳图片384张（382张准确）、未张钳图片434张（416张准确）,模型2的活检钳状态分类准确率为97.56%（798/818）。模型3判断包含病灶图片654张（626张准确）、不包含病灶图片598张（584张准确）,识别有无病灶的准确率为96.65%（1 210/1 252）,以626张模型3准确判断有病灶图片的数据统计模型3的病灶分割效果,平均IoU为0.86（95%CI：0.85～0.87）。（2）数据集3中：活检钳倾斜角度较小状态下系统病灶尺寸测量的平均绝对误差为0.17 mm（95%CI：0.08～0.28 mm）,平均相对误差为3.77%（95%CI：0.00%～10.85%）;活检钳倾斜角度较大状态下系统病灶尺寸测量的平均绝对误差为0.17 mm（95%CI：0.09～0.26 mm）,平均相对误差为4.02%（95%CI：2.90%～5.14%）。（3）数据集4共纳入59例患者的59个内镜检查视频的780张图片,系统病灶尺寸测量的平均绝对误差为0.24 mm（95%CI：0.00～0.67 mm）,平均相对误差为9.74%（95%CI：0.00%～29.83%）。结论　基于人工智能的自动内镜下病灶尺寸测量系统可以实现内镜下对病灶尺寸的准确测量,有望提高内镜医师对病灶尺寸估计的准确率。     

传染病专科医院消化内镜专业医师的培训方法

传染病医院消化内镜操作对象与综合性医院不同,对专业人员的培训有着自身的特点。我们根据在传染病医院进行消化内镜检查和治疗患者的特点,并从传染病防治的角度制定了一套符合传染病医院要求的消化内镜从业人员的培训方法和制度。     

人工智能辅助内镜在胃癌前病变及早期胃癌诊断中的应用进展

慢性萎缩性胃炎、胃黏膜肠上皮化生是重要的胃癌前病变,早期发现并科学管理癌前病变对于胃癌早期防治意义重大。目前为止,内镜检查仍是发现癌及癌前病变的重要手段,但在实际工作中,早期胃癌内镜下表现往往不典型,诊断困难,活检阴性率高,增加了患者不必要的损伤及病理医师的负担。人工智能辅助内镜诊断已成功应用于多种消化道肿瘤的早期诊断,实现了疾病的精准诊断。笔者就目前人工智能辅助内镜诊断在胃癌前病变、早期胃癌诊断中的应用进展进行综述,为不断探索胃癌前病变的精准诊断方法提供有力证据。     

三维可视化技术在消化内镜的应用进展

近年来,数字智能化医学迅速发展,相较于传统的二维成像,三维可视化技术可提供直观、立体的三维图像,便于临床医生多层次、多角度观察病变及其毗邻结构。3D打印技术将可视化图像转化为肉眼可见的物理模型,可进一步提升对复杂疾病形态特征的理解。此外,虚拟现实和混合现实等高级可视化技术,可增加更多真实、互动的医学体验。本文阐述了三维可视化、3D打印及现实技术的基本概念,并对其在消化内镜领域的应用研究进行总结与展望。     

常见消化内镜手术麻醉管理专家共识

随着我国消化内镜诊断和治疗技术的飞速发展,消化内镜的麻醉管理已成为一种专科麻醉。消化内镜手术麻醉的目的是保障消化内镜手术患者的安全,有效防治相关并发症,并为术者提供良好的操作条件。消化内镜手术麻醉管理专家共识从常见的六种消化内镜手术的疾病概况、内镜治疗方法、操作关切点、麻醉前评估和准备、麻醉方法选择、麻醉与消化合作点、苏醒期管理等方面进行详细阐述,期望本共识能够在临床实践中为医师提供切实的帮助,以期有助于无痛消化内镜手术的普及和提高。     

Artificial intelligence in gastrointestinal endoscopy:The future is almost here

Artificial intelligence(AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computeraided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.     

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

With recent breakthroughs in artificial intelligence, computer‐aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early‐stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false‐negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real‐time gastroscopy to provide on‐site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.     

Artificial intelligence in pancreaticobiliary endoscopy

Artificial intelligence (AI) applications in health care have exponentially increased in recent years, and a few of these are related to pancreatobiliary disorders. AI‐based methods were applied to extract information, in prognostication, to guide clinical treatment decisions and in pancreatobiliary endoscopy to characterize lesions. AI applications in endoscopy are expected to reduce inter‐operator variability, improve the accuracy of diagnosis, and assist in therapeutic decision‐making in real time. AI‐based literature must however be interpreted with caution given the limited external validation. A multidisciplinary approach combining clinical and imaging or endoscopy data will better utilize AI‐based technologies to further improve patient care.     

Application of artificial intelligence in gastrointestinal endoscopy

With recent significant improvements in artificial intelligence (AI), especially in the field of deep learning, an increasing number of studies have evaluated the use of AI in endoscopy to detect and diagnose gastrointestinal (GI) lesions. The present review summarizes current publications on the use of AI in GI endoscopy and focuses on the challenges and future of AI‐aided systems. We expect AI to provide an effective and practical method for endoscopists in lesion detection and characterization as well as in quality control in endoscopy. However, so far, most studies have remained at the preclinical stage. More attention should be paid in the future to the use of AI in real‐life clinical applications.     

Evolving role of artificial intelligence in gastrointestinal endoscopy

Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.     

Artificial intelligence in perioperative management of major gastrointestinal surgeries

Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.     

人工智能在胃肠镜质量控制方面的应用

基于深度学习的人工智能已成为一种突破性的计算机技术, 并迅速应用于医学领域。其中, 人工智能辅助系统能够通过提高识别能力、监测检查时间、减少盲点、记录图片、进行肠道准备评分等方式提高胃肠镜的质量。     

Techniques in gastrointestinal endoscopy: surgical endoscopy

Surgical endoscopy training has traditionally resembled an apprenticeship model, with trainees performing an increasing portion of procedures under the direct supervision of an experienced physician. Due to increasingly variable training environments and the widely supported view that numbers alone do not necessarily parallel competency, many objective tools have been developed to educate trainees and ensure that we are training safe and effective endoscopists. This chapter will focus on the tools by which trainees can be objectively and uniformly assessed in their ability to practice safe and proficient surgical endoscopy.