人工智能技术在医学影像专家系统中的应用及发展

本对人工智能技术在常规医学及其医学影像专家系统中的发展情况作了回顾,阐明了在医学诊断系统中,主要困难在于多种疾病的同时并发,既在许多病人中存在着一种疾病的症状潜伏着多种其它疾病的症状的现象。而医学影像专家系统发展的困难在于高级视觉系统内在的不足,从医学扫描器上获得的数据可能是噪声和模糊的,从而增加了专家系统的复杂性。最后对人工智能技术在医学影像专家系统中的发展前景作了展望。     

人工智能技术及其在医学诊断中的应用及发展

本文对人工智能技术在常规医学及其医学诊断专家系统中的发展情况作了回顾，并对人工神经网络在医学诊断系统中的应用作了概述，阐明了在医学诊断系统中，主要的困难在于多种疾病的共存现象，即许多病人有着潜伏在自身内部的其它相关性疾病，而制约医学影像专家系统发展的主要原因是高级视觉系统本身的缺陷，即从医学扫描器上获得的图像数据可能是噪声和模糊的。从而增加了专家系统的复杂性，最后对人工智能技术在医学影像诊断系统中的发展前景作了展望。     

人工智能在医学的应用研究进展

人工智能技术的快速发展,得益于大数据、数据库、算法、算力的巨大进步,医学研究是人工智能的重要应用方向。人工智能与医学的融合发展,提高了医疗技术水平与医疗服务效率,为医生与医疗设备有效赋能,更好地服务于患者。特别在此次新冠肺炎疫情中取得的巨大成效,足见人工智能在医疗领域中发挥巨大作用,因此吸引了许多研究者不断深入探索。本文对近年来人工智能在医学方面应用的相关文献进行梳理,基于人工智能技术与医学研究的发展背景,重点论述人工智能在药物研发、辅助诊疗、语音识别和语义理解、健康管理、医院管理等领域的应用进展,分析人工智能在医疗领域应用存在的挑战,最后讨论人工智能在医疗领域的发展趋势。 【关键词】人工智能;医学应用;技术挑战;综述     

人工智能及机器学习在心血管疾病中的应用

人工智能(AI)及机器学习(ML)因其独特的优势逐渐在医学领域得到了较为广泛的应用。在心血管疾病中,该技术在处理电子病历记录中繁杂的数据,预测分析疾病发展及预后,自动分析和识别心血管影像学及心律失常,发现疾病新亚型等方面已经取得了一定进展。AI及ML在心血管疾病研究中潜力巨大,将会为心血管领域带来全新的突破。     

人工智能方法在医学图像处理中的研究新进展

目的:随着医学影像智能化诊断的快速发展,为了满足愈加复杂的医学图像分析和处理要求,人工智能方法成为近年来医学图像处理技术发展的一个研究热点。本文对近五年来人工智能方法在医学图像处理领域应用的新进展进行综述。方法:将应用在医学图像处理领域主要的几种人工智能方法进行了分类总结,讨论了这些方法在医学图像处理各分支领域的应用,分析比较了不同方法间的优缺点。结果:人工智能方法应用主要在医学图像分割、图像配准、图像融合、图像压缩、图像重建等领域;包括蚁群算法、模糊集合、人工神经网络、粒子群算法、遗传算法、进化计算、人工免疫算法、粒计算和多Agent技术等;涉及MR图像、超声图像、PET图像、CT图像和医学红外图像等多种医学图像。结论:由于医学影像图像对比度较低,不同组织的特征可变性较大,不同组织间边界模糊、血管和神经等微细结构分布复杂,尚无通用方法对任意医学图像都能取得绝对理想的处理效果。改进的人工智能方法与传统图像处理方法的结合,在功能上相互取长补短,将是医学图像处理技术重要的发展趋势。     

项目教学法在护理应用解剖学运动系统实践教学中的应用

项目教学法是一种建立在建构主义学习理论基础上的有别于传统教学的新型教学方法,是教师把教学过程设计成一个或多个项目,并作适当的示范,然后让学生分组围绕项目进行讨论协作学习,以完成项目的情况来评价学生是否达到教学目标的一种教学方法。它是一种以学生主动学习,     

人工智能技术在医学影像专家系统中的应用及发展

本文对人工智能技术在常规医学及其医学影像专家系统中的发展情况作了回顾 ,阐明了在医学诊断系统中 ,主要困难在于多种疾病的同时并发 ,即在许多病人中存在着一种疾病的症状潜伏着多种其它疾病的症状的现象。而医学影像专家系统发展的困难在于高级视觉系统内在的不足 ,从医学扫描器上获得的数据可能是噪声和模糊的 ,从而增加了专家系统的复杂性。最后对人工智能技术在医学影像专家系统中的发展前景作了展望。     

“举例”教学形式在运动人体科学教学中的应用

通过文献资料法和教学实践,针对体育生的学习基础和对运动人体科学课程的学习态度,分析在教学中适当采用联系应用的实例方式传授知识,达到提高学生学习兴趣、促进学生主动参与教学过程、加大学生对知识的接受程度,提高教学质量的目的。     

Blackboard网络教学平台在运动人体科学中的应用

通过文献资料法并结合教学实践,分析Blackboard网络教学平台的特点、组成及使用中的注意问题,探讨其在运动人体科学教学中的应用,并充分利用其灵活、及时、互动、个性化的特征,使课堂教学在网络上得以重现、补充和延伸,以达到分层教学、提高教学质量的目的。     

人工智能在创伤骨科中的应用

新形势、新环境下,创伤骨科医师面临伤情更加复杂、人口老龄化加剧的严峻挑战。随着数据处理能力的飞速发展、医工交叉的深入,人工智能的应用正逐步延伸至医学各相关领域,同样也是未来创伤骨科的发展重要方向,将为创伤骨科面临的问题提出新的解决途径。人工智能将在诊断、处理、教育与研究、系统分析等领域促进创伤骨科的发展。然而,人工智能在创伤骨科的应用离不开数据安全性、稳定性的影响,仍然面临一些问题。本文就人工智能的概念及在创伤骨科中的应用与挑战做一综述,旨在汲取国内外先进的人工智能理念及先进技术,以期提升其在创伤骨科的应用,为患者提供精准化、个性化的诊疗服务。     

人工智能技术在电生理诊断中的应用现状

人工智能理论及技术的不断发展,使其在各行业各领域有着广泛应用。电生理检查通过仪器设备获取人体生物电信号,并以图像形式展现,医师依据图像特征判断和分析疾病。借助人工智能技术自动分析电生理图像信号,能够实现疾病的智能诊断与预测,减少对医师经验的依赖,提升医疗服务水准。本文对人工智能技术在电生理诊断中的研究与应用现状进行综述,为电生理学科的发展提供一定的支撑和参考。     

An artificial intelligence framework for compensating transgressions and its application to diet management

Today, there is considerable interest in personal healthcare. The pervasiveness of technology allows to precisely track human behavior; however, when dealing with the development of an intelligent assistant exploiting data acquired through such technologies, a critical issue has to be taken into account; namely, that of supporting the user in the event of any transgression with respect to the optimal behavior. In this paper we present a reasoning framework based on Simple Temporal Problems that can be applied to a general class of problems, which we called cake&carrot problems, to support reasoning in presence of human transgression. The reasoning framework offers a number of facilities to ensure a smart management of possible “wrong behaviors” by a user to reach the goals defined by the problem.This paper describes the framework by means of the prototypical use case of diet domain. Indeed, following a healthy diet can be a difficult task for both practical and psychological reasons and dietary transgressions are hard to avoid. Therefore, the framework is tolerant to dietary transgressions and adapts the following meals to facilitate users in recovering from such transgressions. Finally, through a simulation involving a real hospital menu, we show that the framework can effectively achieve good results in a realistic scenario.     

Application and performance of artificial intelligence technology in cytopathology

Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent advancements in computer vision and machine learning. The first use of AI in a pathology lab was in cytopathology, when a computer-assisted Pap test screening was created. Initially designed to diagnose rather than screen, there was a lot of disagreement concerning their wide use to clinical specimens. However, whole-slide imaging of both gynaecological and non-gynaecological histopathology have been the subject of recent AI work. An overview of the literature on AI in cytopathology is provided in this brief review. To be more precise, it intends to emphasize the relevance of applications of AI algorithms to gynaecological and non-gynaecologic cytology. Between January 2000 and December 2021, a search on artificial intelligence in cytopathology was conducted in several well-known databases, including PubMed, Web of Science, Scopus, Embase, and Google Scholar. Only full-text papers that could be accessed online were evaluated.     

人工智能在中医诊断中的应用进展

本研究立足于各类人工智能算法的数学原理,阐述了人工智能在中医诊断中的应用现状及问题。其中传统机器学习算法,如支持向量机、贝叶斯算法等因其小样本学习的特性,在闻诊、问诊等场景具备较高的精度与稳健性;而近年来新兴的深度学习算法则可以处理如图像、音频信号、文本等非结构化数据,与望诊、切诊等场景相契合;多模态深度学习则可以充分挖掘望闻问切数据中的信息,并在特征空间中进行隐式的四诊合参。人工智能的引入可以进一步推动中医的客观化、定量化发展,但其数据驱动的特性要求进一步规范现行的中医数据库建立流程。     

Image analysis and artificial intelligence in infectious disease diagnostics

BackgroundMicrobiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears and histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses.ObjectivesTo review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.SourcesMaterial sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed.ContentWe describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.ImplicationsCombined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.     

NMRES: An artificial intelligence expert system for quantification of cardiac metabolites from31phosphorus nuclear magnetic resonance spectroscopy

The application of high-resolution³¹Phosphorus Nuclear Magnetic Resonance (³¹P NMR) Spectroscopy in biology and medicine has provided new insights into biochemical processes and also a unique assessment of metabolites. However, accurate quantification of biological NMR spectra is frequently complicated by: (a) non-Lorentzian form of peak lineshapes, (b) contamination of peak signals by neighboring peaks, (c) presence of broad resonances, (d) low signal-to-noise ratios, and (e) poorly defined sloping baselines. Our objectives were to develop an expert system that captures and formalizes³¹P NMR spectroscopists' expert knowledge, and to provide a reliable, efficient, and automated system for the interpretation of biological spectra. The NMR Expert System (NMRES) was written in the C and OPS5 programming languages and implemented on a Unix-based (Ultrix) mainframe system with XWindows bitmap graphics display. Expert knowledge was acquired from NMR spectroscopists and represented as production rules in the knowledge base. A heuristic weights method was employed to determine the confidence levels of potential peaks. Statistical and numerical methods were used to facilitate processing decisions. NMR spectra obtained from studies of ischemic neonatal and immature hearts were used to assess the performance of the expert system. The expert system performed signal extraction, noise treatment, resonance assignment, intracellular pH determination, and metabolite intensity quantitation in about 10 s per 4 KB (kilobyte) spectrum. The peak identification success rate was 98.2%. Peak areas and pH estimated by the expert system compared favorably with those determined by human experts. We conclude that the expert system has provided a framework for reliable and efficient quantification of complex biological³¹P NMR spectra. In 1988, this work was presented in part at the IEEE Engineering in Medicine and Biology Society Tenth Annual International Conference, New Orleans, LA; the Society of Magnetic Resonance in Medicine Seventh Annual Meeting, San Francisco, CA; and in 1989, at the AAAS 155th Annual National Meeting, San Francisco, CA. J.L. Chow was the recipient of the First Place Award for Excellence for the best graduate student paper presented in the Section on Physical Sciences.