2019年启航献礼

精准医疗是应用现代分子生物学、分子病理学、分子遗传学、分子影像技术、生物信息技术以及目前火热的大数据技术、智能化技术等,结合患者生活环境和临床数据,实现精准的疾病分类和诊断,制定具有个性化的疾病预防和诊疗方案,包括对风险的精确预测、疾病精确诊断、疾病精确分类、药物精确应用、疗效精确评估、疗后精确预测等。精准医疗是医学自身发展的客观必然,是人民群众对健康新需求的使然。精准医疗的核心价值是造福于患者,造福于人类,尤其是在当今中国,人民生活水平普遍得到改善和提高,人民对健康的追求达到了一个新的高度。     

术中精准医学诊疗方法——光学分子影像手术导航技术

<正>随着医学影像技术的发展,光学分子影像手术导航技术作为一种新兴的医学影像方法,可以辅助医生在实施肿瘤切除的过程中,精确定位肿瘤的边界。临床实验表明,该方法能够为医生实施精准手术提供强大的帮助,并提高患者的术后生存率。文章主要综述了目前光学分子影像手术导航系统的进展,以期为临床精准医学手术治疗提供一种有效的方法。     

精准医疗与伴随诊断产业发展研究

<正>1概述1.1精准医疗精准医疗被誉为医疗的未来,它基于人们在基因、环境、工作与生活方式的个体差异,应用现代分子生物学、分子病理学、分子遗传学、分子影像技术、生物信息技术以及目前热门的大数据技术、人工智能技术等,通过对患者的生活环境和临床数据等进行分析,实现精准的疾病分类和诊断,制订具有个性化的疾病预防和诊疗方案。精准医疗包括对风险的精确预测、疾病精确诊断、疾病精确分类、药物精确应用、疗效精确评估与疗后精确预测。精准医疗是科学技术及医学自身发展的客观必然,也是广大民众摆脱贫困以后对健康、教育、生活质量追求的必然。因     

推动精准医疗和伴随诊断产业创新发展

正精准医疗,就是应用现代分子生物学、分子病理学、分子遗传学、分子影像技术、生物信息技术以及目前热门的大数据技术、智能化技术等,结合患者生活环境和临床数据,实现精准的疾病分类和诊断,制定具有个性化的疾病预防和诊疗方案。包括对风险的精确预测,疾病精确诊断,疾病精确分类,药物精确应用,疗效精确评估,疗后精确     

基于机器学习的肠道菌群数据建模与分析研究综述

人体肠道菌群与人类的健康和疾病存在密切关系,对肠道菌群的宏基因组数据进行建模和分析,在疾病预测及诊断相关领域科学研究和社会应用方面均具有重要意义。本文从大数据分析和机器学习的角度,对人体肠道菌群数据的建模、分析和预测算法的原理、过程以及典型研究应用实例进行综述,以期推动肠道菌群分析相关研究发展以及探索结合机器学习算法进行肠道菌群分析的有效方式,同时也为开发基于肠道菌群数据的新型诊疗手段提供借鉴,推动我国精准医疗事业发展。     

分子影像技术在转化医学中的应用

基础医学、药物研发和临床医学是三个不同的的领域,因此这些领域的很多生命科学研究成果经常无法及时应用于临床实践。转化医学是以疾病为中心,加速将基础研究的成果用于,临床诊断和治疗中,旨在有效的将三个领域有机结合在一起。分子影像学（molecularimaging,MI）可在活体上、在细胞和分子水平对生物学过程成像并进行定性和定量研究,为转化医学的实现提供了保证。分子影像技术采用无创的医学影像技术使活体状态下组织细胞中的特殊分子生物学特性得以直观揭示,主要用于对疾病早期诊断、疾病分期（分层）、疗效监测、指导疾病的个体化治疗以及新药的研发等领域。本文主要介绍分子影像的技术特点、其在转化医学中发挥的作用以及其在个体化治疗中临床意义进行综述。     

分子影像技术在转化医学中的应用*

摘要：基础医学、药物研发和临床医学是三个不同的的领域,因此这些领域的很多生命科学研究成果经常无法及时应用于临床实 践。转化医学是以疾病为中心,加速将基础研究的成果用于临床诊断和治疗中,旨在有效的将三个领域有机结合在一起。分子影 像学(molecular imaging, MI) 可在活体上、在细胞和分子水平对生物学过程成像并进行定性和定量研究,为转化医学的实现提供 了保证。分子影像技术采用无创的医学影像技术使活体状态下组织细胞中的特殊分子生物学特性得以直观揭示,主要用于对疾 病早期诊断、疾病分期（分层）、疗效监测、指导疾病的个体化治疗以及新药的研发等领域。本文主要介绍分子影像的技术特点、其 在转化医学中发挥的作用以及其在个体化治疗中临床意义进行综述。     

规范医学影像诊断报告书写,提高医学生及住院医师临床技能

医学影像诊断报告是正确行使医疗行为和保证医疗质量的依据,书写高质量的医学影像学诊断报告是医学生及住院医师必须掌握的一项基本功和基本技能。本文将结合我院长期的医学生及住院医师临床教学经验和实际,以神经系统影像报告为例,从医学影像诊断报告的重要性、医学生及住院医师医学影像诊断报告技能教学的现状、规范化医学影像报告书写技能的培养、评估、反馈和评价进行探讨,以期在医学生和住院医师的培养中提高其临床技能,供广大同道在医学教育与实践中借鉴和改进。     

导读与评议(2017年第6期)

正精准医疗概念的提出开启了一个医学新时代,其实质包括精准诊断和精准治疗。张文宏课题组围绕结核病治疗中的精准医疗进行了阐述,涉及结核病的精准诊断,包括结核病的临床诊断及结核分枝杆菌的检测(分子检测及耐药检测技术等)、特殊人群的药理学参数与药物代谢相关的分子标记、针对病原体生命周期分子靶点的直接作用药物研发、通     

多核磁共振19F-MRI在分子影像学中的应用

随着杂核氟、钠、磷等探针和成像技术的发展以及磁共振成像设备和序列的优化,多核磁共振迅速崛起,尤其是其在分子影像方面的研究与应用使包括心血管、肿瘤等众多疾病从传统的形态学影像诊断模式转向早期分子影像精准诊治模式。其中,19F-MRI多核磁共振分子成像近年来备受瞩目。虽然19F-MRI的成像敏感度是1H-MRI的82%,但人体只有牙齿中含有少量的氟,因此无背底噪声的干扰。19F-MRI应用氟类探针,19F自然丰度100%,且无放射性。本文简述了多核磁共振在分子影像学中的应用,并重点介绍19F-MRI分子影像及其应用探针在精准诊治方面的应用。     

Artificial intelligence and machine learning for medical imaging: A technology review

Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence of disruptive technical advances and impressive experimental results, notably in the field of image analysis and processing. In medicine, specialties where images are central, like radiology, pathology or oncology, have seized the opportunity and considerable efforts in research and development have been deployed to transfer the potential of AI to clinical applications. With AI becoming a more mainstream tool for typical medical imaging analysis tasks, such as diagnosis, segmentation, or classification, the key for a safe and efficient use of clinical AI applications relies, in part, on informed practitioners. The aim of this review is to present the basic technological pillars of AI, together with the state-of-the-art machine learning methods and their application to medical imaging. In addition, we discuss the new trends and future research directions. This will help the reader to understand how AI methods are now becoming an ubiquitous tool in any medical image analysis workflow and pave the way for the clinical implementation of AI-based solutions.     

Precision medicine in human heart modeling

Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

     

A Review of the Deep Learning Methods for Medical Images Super Resolution Problems

Super resolution problems are widely discussed in medical imaging. Spatial resolution of medical images are not sufficient due to the constraints such as image acquisition time, low irradiation dose or hardware limits. To address these problems, different super resolution methods have been proposed, such as optimization or learning-based approaches. Recently, deep learning methods become a thriving technology and are developing at an exponential speed. We think it is necessary to write a review to present the current situation of deep learning in medical imaging super resolution. In this paper, we first briefly introduce deep learning methods, then present a number of important deep learning approaches to solve super resolution problems, different architectures as well as up-sampling operations will be introduced. Afterwards, we focus on the applications of deep learning methods in medical imaging super resolution problems, the challenges to overcome will be presented as well.     

深度学习在药物活性预测研究中的应用

药物从研发到临床应用需要耗费较长的时间，研发期间的投入成本可高达十几亿元。而随着医药研发与人工智能的结合以及生物信息学的飞速发展，药物活性相关数据急剧增加，传统的实验手段进行药物活性预测已经难以满足药物研发的需求。借助算法来辅助药物研发，解决药物研发中的各种问题能够大大推动药物研发进程。传统机器学习方法尤其是随机森林、支持向量机和人工神经网络在药物活性方面能够达到较高的预测精度。深度学习由于具有多层神经网络，模型可以接收高维的输入变量且不需要人工限定数据输入特征，可以拟合较为复杂的函数模型，应用于药物研发可以进一步提高各个环节的效率。在药物活性预测中应用较为广泛的深度学习模型主要是深度神经网络（deep neural networks，DNN）、循环神经网络（recurrent neural networks，RNN）和自编码器（auto encoder，AE），而生成对抗网络（generative adversarial networks，GAN）由于其生成数据的能力常常被用来和其他模型结合进行数据增强。近年来深度学习在药物分子活性预测方面的研究和应用综述表明，深度学习模型的准确度和效率均高于传统实验方法和传统机器学习方法。因此，深度学习模型有望成为药物研发领域未来十年最重要的辅助计算模型。     

基于机器学习的药物-靶标相互作用预测*

近年来,随着计算机硬件、软件工具和数据丰度的不断突破,以机器学习为代表的人工智能技术在生物、基础医学和药学等领域的应用不断拓展和融合,极大地推动了这些领域的发展,尤其是药物研发领域的变革。其中,药物-靶标相互作用(drug-target interactions, DTI)的识别是药物研发领域中的重要难题和人工智能技术交叉融合的热门方向,研究人员在DTI预测方面做了大量的工作,构建了许多重要的数据库,开发或拓展了各类机器学习算法和工具软件。对基于机器学习的DTI预测的基本流程进行了介绍,并对利用机器学习预测DTI的研究进行了回顾,同时对不同的机器学习方法运用于DTI预测的优缺点进行了简单总结,以期对开发更加有效的预测算法和DTI预测的发展提供帮助。     

Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.     

生物标记物与精准医疗研究进展

自2015年精准医疗理念提出后,生物标记物与精准医疗愈加被人们重视。并被广泛应用于病毒、癌症等重大疾病的筛查与治疗研究中。新兴循环生物标记物的应用与开发,在HBV、HPV的检测和肿瘤的靶向药物治疗、细胞治疗、免疫抑制剂、癌症疫苗开发方向上都取得了重大成果。与传统的医疗方法相比,生物标记物为辅助精准医疗的检测治疗模式,无论是在研发还是治疗上都有着显著的优势。因此,综述了生物标记物与精准医疗的应用,并就最近的研究报告重点综述了生物标记物与精准医疗的最新研究进展。     

A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

The globe's population is increasing day by day, which causes the severe problem of organic food for everyone. Farmers are becoming progressively conscious of the need to control numerous essential factors such as crop health, water or fertilizer use, and harmful diseases in the field. However, it is challenging to monitor agricultural activities. Therefore, precision agriculture is an important decision support system for food production and decision-making. Several methods and approaches have been used to support precision agricultural practices. The present study performs a systematic literature review on hyperspectral imaging technology and the most advanced deep learning and machine learning algorithm used in agriculture applications to extract and synthesize the significant datasets and algorithms. We reviewed legal studies carefully, highlighted hyperspectral datasets, focused on the most methods used for hyperspectral applications in agricultural sectors, and gained insight into the critical problems and challenges in the hyperspectral data processing. According to our study, it has been found that the Hyperion hyperspectral, Landsat-8, and Sentinel 2 multispectral datasets were mainly used for agricultural applications. The most applied machine learning method was support vector machine and random forest. In addition, the deep learning-based Convolutional Neural Networks (CNN) model is mainly used for crop classification due to its high performance with hyperspectral datasets. The present review will be helpful to the new researchers working in the field of hyperspectral remote sensing for agricultural applications with a machine and deep learning methods.     

Machine learning in Magnetic Resonance Imaging: Image reconstruction

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction.A wide range of approaches have been proposed, which can be applied in k-space and/or image-space.Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation.In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends.     

利用人工智能预测癌症的易感性、复发性和生存期

癌症具有较高的发病率和致死率,对人类健康具有重大威胁。癌症预后分析可以有效避免过度治疗及医疗资源的浪费,为医务人员及家属进行医疗决策提供科学依据,已成为癌症研究的必要条件。随着近年来人工智能技术的迅速发展,对癌症患者的预后情况进行自动化分析成为可能。此外,随着医疗信息化的发展,智慧医疗的理念受到广泛关注。癌症患者作为智慧医疗的重要组成部分,对其进行有效的智能预后分析十分必要。本文综述现有基于机器学习的癌症预后方法。首先,对机器学习与癌症预后进行概述,介绍癌症预后及相关的机器学习方法,分析机器学习在癌症预后中的应用;然后,对基于机器学习的癌症预后方法进行归纳,包括癌症易感性预测、癌症复发性预测、癌症生存期预测,梳理了它们的研究现状、涉及到的癌症类型与数据集、用到的机器学习方法及预后性能、特点、优势与不足;最后,对癌症预后方法进行总结与展望。