医学专家系统的设计原理与实现方法

医学专家系统是人工智能和专家系统理论和技术在医学领域的重要应用，具有极大的科研和应用价值，它可以帮助医生解决复杂的医学问题，作为医生诊断，治疗的辅助工具。本文阐述了医学专家系统的设计原理与实现方法，总结了当今医学专家系统存在的问题，展望了未来的发展。     

医学诊断专家系统进展

专家系统是人工智能领域的重要分支。医学诊断专家系统可以作为医生诊断的一种辅助工具。本文以医学诊断专家系统理论研究、专项医学诊断专家系统和神经网络专家系统等三方面，对医学诊断专家系统的进展进行了回顾。     

医学诊断专家系统进展

专家系统是人工智能领域的重要分支。医学诊断专家系统可以作为医生诊断的一种辅助工具。本以医学诊断专家系统理论研究、专项医学诊断专家系统和神经网络专家系统等三方面，对医学诊断专家系统的进展进行了回顾。     

医学图像分割技术研究

医学图像分割技术是医学图像处理和分析中的关键技术.医学图像分割在医学诊断中扮演着重要角色,是图像分割的一个重要应用领域.本文综述和讨论了近年来的医学图像发展概况、分割技术、研究热点及其医学图像分割的评价等问题,并简要讨论了每类分割方法的特点及医学图像分割发展趋势.     

图像分析系统及其在生物医学中的应用

目前图像分析系统在组织细胞形态与化学成份定量分析研究工作和辅助临床病理诊断中起着重要的作用。随着计算机技术的快速发展，图像分析系统也有了长足的进步，其测试功能，速度，精度及自动化程度有了大幅度的提高，同时，医学的进步，大大推动了医学图像分析系统的发展，本文就图像分析系统的发展过程，基本结构和工作原理作了着重阐明，并介绍了图像分析系统在生物医学中的应用。     

医学数据挖掘的技术、方法及应用

医学数据挖掘是提高医院信息管理水平，为疾病的诊断和治疗提供科学的、准确的决策，促进远程医疗和社区医疗发展的需要。本文对医学数据挖掘的关键技术——数据的预处理、多属性信息的融合、挖掘算法的高效性与鲁棒性、提供知识的准确性与可靠性等进行了论述；阐述了基于计算智能的医学数据挖掘方法，介绍了人工神经网络、模糊逻辑、遗传算法、粗糙集理论和支持向量机在医学数据挖掘中的应用；最后对医学数据挖掘的特点和亟待解决的问题进行了总结。     

多模态医学图像配准技术的分类与研究进展

多模态医学图像配准技术是目前医学图像处理中的研究热点 ,对于临床诊断和治疗有重要意义。本文首先分析了图像配准的过程 ,并在此基础上对配准方法进行了反映其本质的分类 ,然后综述了目前的一些主要的多模态医学图像的配准方法 ,最后提出了医学图像配准研究中的几个主要问题。     

基于深度学习的甲状腺疾病超声图像诊断研究综述

近年来，甲状腺疾病的发病率显著升高，超声检查是甲状腺疾病诊断的首选检查手段。同时，基于深度学习的医疗影像分析水平快速提高，超声影像分析取得了一系列里程碑式的突破，深度学习算法在医学图像分割和分类领域展现出强大的性能。本文首先阐述了深度学习算法在甲状腺超声图像分割、特征提取和分类分化三个方面的应用，其次对深度学习处理多模态超声图像的算法进行归纳总结，最后指出现阶段甲状腺超声图像诊断存在的问题，展望未来发展方向，以期促进深度学习在甲状腺临床超声图像诊断中的应用，为医生诊断甲状腺疾病提供参考。     

基于深度学习技术的磁共振成像研究进展

磁共振（MR）成像是当前应用于临床医学诊断的重要医学成像手段之一。如何缩短扫描时间进行快速成像一直以来都是MR成像领域中的热门研究问题。近年来，随着深度学习的兴起和快速发展，深度学习被广泛应用于医学图像处理领域中。目前基于深度学习的MR成像方法作为MR成像的新兴方向，相应的研究已取得了一系列进展。本文对几种常见的基于深度学习的MR成像方法进行归纳和简要分析，并对其研究前景进行了展望。     

医学图像的色彩处理

彩色图像显然比单色（黑白）图象更好看，由于大量的医学图像是单色图像，因此将其处理成彩色图像，将有助于医务人员对疾病的诊断与治疗。医学图像的彩色处理方法很多，作者借助于计算机用映射变换法来处理医学图像的色彩问题－伪彩色处理，并详细讨论了转换原理及转换方法。     

Evidence-based medicine, medical decision analysis, and pathology

Recent advances in molecular pathology and other technologies such as proteomics present pathologists with the challenge of integrating the new information generated with high-throughput methods with current diagnostic models based mostly on histopathology and clinicopathologic correlations. Parallel developments in the field of medical informatics and bioinformatics provide the technical and mathematical methods to approach these problems in a rational manner. However, it remains unclear whether pathologists or other medical specialists will become primarily responsible for the development and maintenance of these multivariate and multidisciplinary diagnostic and prognostic models that are hoped to provide more accurate, individualized patient-based information. Evidence-based medicine (EBM) and medical decision analysis (MDA) are relatively new disciplines that use quantitative methods to assess the value of information, differentiate fact from myth, and integrate so-called best evidence into multivariate models for the assessment of prognosis, response to therapy, selection of laboratory tests, and other complex problems that influence individual patient care. We review from an epistemological viewpoint the current approach to information in pathology and describe some of the concepts developed by the practitioners of EBM and MDA.     

Quantitative Imaging Biomarkers: The Application of Advanced Image Processing and Analysis to Clinical and Preclinical Decision Making

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.     

Improving diagnostic accuracy using a hierarchical neural network to model decision subtasks

A number of quantitative models including linear discriminant analysis, logistic regression, k nearest neighbor, kernel density, recursive partitioning, and neural networks are being used in medical diagnostic support systems to assist human decision-makers in disease diagnosis. This research investigates the decision accuracy of neural network models for the differential diagnosis of six erythematous-squamous diseases. Conditions where a hierarchical neural network model can increase diagnostic accuracy by partitioning the decision domain into subtasks that are easier to learn are specifically addressed. Self-organizing maps (SOM) are used to portray the 34 feature variables in a two dimensional plot that maintains topological ordering. The SOM identifies five inconsistent cases that are likely sources of error for the quantitative decision models; the lower bound for the diagnostic decision error based on five errors is 0.0140. The traditional application of the quantitative models cited above results in diagnostic error levels substantially greater than this target level. A two-stage hierarchical neural network is designed by combining a multilayer perceptron first stage and a mixture-of-experts second stage. The second stage mixture-of-experts neural network learns a subtask of the diagnostic decision, the discrimination between seborrheic dermatitis and pityriasis rosea. The diagnostic accuracy of the two stage neural network approaches the target performance established from the SOM with an error rate of 0.0159.     

Development of an integrated Sasang constitution diagnosis method using face, body shape, voice, and questionnaire information

ABSTRACT: BACKGROUND: Sasang constitutional medicine (SCM) is a unique form of traditional Korean medicine that divides human beings into four constitutional types (Tae-Yang: TY, Tae-Eum: TE, So-Yang: SY, and So-Eum: SE), which differ in inherited characteristics, such as external appearance, personality traits, susceptibility to particular diseases, drug responses, and equilibrium among internal organ functions. According to SCM, herbs that belong to a certain constitution cannot be used in patients with other constitutions; otherwise, this practice may result in no effect or in an adverse effect. Thus, the diagnosis of SC type is the most crucial step in SCM practice. The diagnosis, however, tends to be subjective due to a lack of quantitative standards for SC diagnosis. METHODS: We have attempted to make the diagnosis method as objective as possible by basing it on an analysis of quantitative data from various Oriental medical clinics. Four individual diagnostic models were developed with multinomial logistic regression based on face, body shape, voice, and questionnaire responses. Inspired by SCM practitioners' holistic diagnostic processes, an integrated diagnostic model was then proposed by combining the four individual models. RESULTS: The diagnostic accuracies in the test set, after the four individual models had been integrated into a single model, improved to 64.0% and 55.2% in the male and female patient groups, respectively. Using a cut-off value for the integrated SC score, such as 1.6, the accuracies increased by 14.7% in male patients and by 4.6% in female patients, which showed that a higher integrated SC score corresponded to a higher diagnostic accuracy. CONCLUSIONS: This study represents the first trial of integrating the objectification of SC diagnosis based on quantitative data and SCM practitioners' holistic diagnostic processes. Although the diagnostic accuracy was not great, it is noted that the proposed diagnostic model represents common rules among practitioners who have various points of view. Our results are expected to contribute as a desirable research guide for objective diagnosis in traditional medicine, as well as to contribute to the precise diagnosis of SC types in an objective manner in clinical practice.     

Application of grey system theory in telecare

As a superiority to conventional statistical models, grey models require only a limited amount of data to estimate the behaviour of unknown systems. Grey system theory can be used in the effective factor assessment, and used in large samples where data are not available or uncertain whether the data was representative. Therefore, the purpose of this study was to adopt grey system theory to discuss older adult users' opinions on the telecare and its effect on their quality of life. This study surveyed the older adult users of Taiwan as subjects. User perception of the telecare services was collected via face-to-face interview. The grey system theory was used to examine the model. The results showed that the overall living quality has the greatest effect on the perceived effects of the telecare on their quality of life, followed by the acquisition of information, accessibility of medical care services, and safety. This finding may serve as a reference to future studies and it also shows that the grey system theory is a feasible analysis method.     

小细胞肺癌患者胸水、血清中NSE、 pro-GRP定量分析与免疫组化分析的诊断一致性评价

目的 评价小细胞肺癌（SCLC）患者胸水、血清中NSE、pro-GRP定量分析与免疫组化分析的诊断一致性.方法 采用免疫组化方法对病理标本的NSE、pro-GRP表达进行分析,用化学发光法对血清和胸水NSE、pro-GRP表达水平做定量分析.结果 小细胞肺癌患者血清和胸水NSE、pro-GRP表达水平明显高于非小细胞肺癌（NSCLC）患者.NSE、pro-GRP免疫组化分析与血清和胸水NSE、pro-GRP定量分析诊断阳性率无统计学差异.结论 小细胞肺癌患者手术前缺乏病理组织材料时,可定量分析血清和胸水NSE、pro-GRP表达水平获得与免疫组化分析一致的诊断效果.     

Towards a genuinely medical model for psychiatric nosology

Psychiatric nosology is widely criticized, but solutions are proving elusive. Planned revisions of diagnostic criteria will not resolve heterogeneity, comorbidity, fuzzy boundaries between normal and pathological, and lack of specific biomarkers. Concern about these difficulties reflects a narrow model that assumes most mental disorders should be defined by their etiologies. A more genuinely medical model uses understanding of normal function to categorize pathologies. For instance, understanding the function of a cough guides the search for problems causing it, and decisions about when it is expressed abnormally. Understanding the functions of emotions is a foundation missing from decisions about emotional disorders. The broader medical model used by the rest of medicine also recognizes syndromes defined by failures of functional systems or failures of feedback control. Such medical syndromes are similar to many mental diagnoses in their multiple causes, blurry boundaries, and nonspecific biomarkers. Dissatisfaction with psychiatric nosology may best be alleviated, not by new diagnostic criteria and categories, but by more realistic acknowledgment of the untidy landscape of mental and other medical disorders.     

基于灰色关联分析的中医证候推理诊断方法

针对中医诊断专家系统存在的问题,通过模拟专家诊断的思维过程,引入征候筛选和灰色关联分析模型对诊断过程进行模拟和评价,实现了一个新的计算机化中医诊断方法。     

Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models

There are a number of different quantitative models that can be used in a medical diagnostic decision support system including parametric methods (linear discriminant analysis or logistic regression), nonparametric models (k nearest neighbor or kernel density) and several neural network models. The complexity of the diagnostic task is thought to be one of the prime determinants of model selection. Unfortunately, there is no theory available to guide model selection. This paper illustrates the use of combined neural network models to guide model selection for diagnosis of ophthalmic and internal carotid arterial disorders. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models.