Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Research Status of TCM Rehabilitation in Chronic Obstructive Pulmonary Disease (COPD)

This paper mainly analyzes the application status of TCM rehabilitation in chronic obstructive pulmonary disease(COPD),hoping to provide support and help for clinical staff through this study,and promote the further development of COPD rehabilitation program.

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Although artificial intelligence(AI)was initially developed many years ago,it has experienced spectacular advances over the last 10 years for application in the field of medicine,and is now used for diagnostic,therapeutic and prognostic purposes in almost all fields.Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma(HCC),as this is a very common tumor,with particular radiological characteristics that allow its diagnosis without the need for a histological study.However,the interpretation and analysis of the resulting images is not always easy,in addition to which the images vary during the course of the disease,and prognosis and treatment response can be conditioned by multiple factors.The vast amount of data available lend themselves to study and analysis by AI in its various branches,such as deeplearning(DL)and machine learning(ML),which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation.ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns.DL is a more extensive form of learning that attempts to simulate the working of the human brain,using a lot more data and more complex algorithms.This review specifies the type of AI used by the various authors.However,welldesigned prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice.In addition,professionals now need to understand the true usefulness of these techniques,as well as their associated strengths and limitations.

Development of artificial intelligence technology in diagnosis,treatment,and prognosis of colorectal cancer

Artificial intelligence(AI)technology has made leaps and bounds since its invention.AI technology can be subdivided into many technologies such as machine learning and deep learning.The application scope and prospect of different technologies are also totally different.Currently,AI technologies play a pivotal role in the highly complex and wide-ranging medical field,such as medical image recognition,biotechnology,auxiliary diagnosis,drug research and development,and nutrition.Colorectal cancer(CRC)is a common gastrointestinal cancer that has a high mortality,posing a serious threat to human health.Many CRCs are caused by the malignant transformation of colorectal polyps.Therefore,early diagnosis and treatment are crucial to CRC prognosis.The methods of diagnosing CRC are divided into imaging diagnosis,endoscopy,and pathology diagnosis.Treatment methods are divided into endoscopic treatment,surgical treatment,and drug treatment.AI technology is in the weak era and does not have communication capabilities.Therefore,the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients.This article reviews the application of AI in the diagnosis,treatment,and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Adoption of Artificial Intelligence for Diagnosis and Treatment of <i>Staphylococcus aureus</i>Infections Disease on Humans

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human. The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care. The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals. Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL. The result after design was a computerized standalone application that assists health practitioners (Doctor’s) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin. The expert system will facilitate quick decision making in the clinic.

Interpretation of diagnosis and treatment scheme of COVID-19 in Hunan province (the third trial version) based on three factors and measures

The similarities and differences between"Novel Coronavirus Infected pneumonia in Hunan province(trial second edition)"and"Novel Coronavirus Infected pneumonia(Trial seventh edition)"released by the National Health Commission and their ideas were interpreted based on"suiting measures to three factors".①In Hunan program,the third version of the trial adjustment more suitable for local characteristics of the disease;②The prevention program population classification is clear,support,eliminate pathogenic factors of the primary and secondary distinct;③The cause of disease is prominent,damp and toxin are mixed with dryness,and the treatment is mainly to moisten the lung and clear away heat,and the prescription is mostly Xinliang light agent.

Meta-analysis of the diagnosis and treatment of traditional medicine for acute coronary syndrome

The morbidity and mortality of acute coronary syndrome disease is extremely high and is the main cause of human death.As an important part of the wisdom of the Chinese nation,TCM plays an important role in treatment.By reviewing recent related research and reports on TCM theories,Chinese herbal formulas,Chinese and Western medicine treatment and other considerations,it aims to provide ideas and references for clinical treatment.

Application of artificial intelligence in liver diseases: From diagnosis to treatment

Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden.There were an estimated 2.14 million liver-related deaths in 2017,representing an 11.4%increase since 2012.Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease.As a hot research topic in recent years,the application of artificial intelligence(AI)in different fields has attracted extensive attention,and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases.Machine learning(ML)is the core of AI and the basic way to make a computer intelligent.ML technology has many potential uses in hepatology,ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis.The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly,accurately and scientifically,which is of importance for reducing the incidence and mortality of liver diseases,reducing medical errors,and promoting the development of medicine.This paper reviews the application and prospects of AI in liver diseases,and aims to improve clinicians’awareness of the importance of AI in the diagnosis and treatment of liver diseases.

Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma:Current status and future perspectives

Hepatocellular carcinoma(HCC)is the most common primary malignant liver tumor in China.Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions.Artificial intelligence(AI),such as machine learning(ML)and deep learning,has recently gained attention for its capability to reveal quantitative information on images.Currently,AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine.This review summarizes the applications of AI in various aspects of preoperative imaging of HCC,including segmentation,differential diagnosis,prediction of histo-pathology,early detection of recurrence after curative treatment,and evaluation of treatment response.We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

女性盆底功能障碍性疾病诊治流程及物理治疗康复临床应用——康复流程、原则及智能诊疗

女性盆底功能障碍性疾病(FPFD)发生机制复杂,专科的临床医师往往无法覆盖妇产、泌尿、肛肠、影像等各专科全面的医学专业信息,限制了对于该疾病的系统、全面诊治。FPFD的治疗需要遵循康复流程,制定个性化物理治疗干预措施,同时结合其他方式制定综合性治疗策略。人工智能在盆底领域的应用,不仅可以提高医生诊断效率,还可以为综合治疗方案的制定提供参考和依据,实现智能诊疗与精准医疗。本文从FPFD康复流程出发,分析康复原则及内容,探究人工智能在FPFD诊疗中的应用现状,旨在为实现盆底疾病智能诊疗提供参考。

ChatGPT之于临床医生是助手而非替手

随着人工智能技术的发展和医疗需求的不断提高,人工智能工具在医学领域的应用不断增加。ChatGPT自问世后受到医学界的广泛关注,在临床诊疗、医学科研、医学教育等方面发挥了越来越多的作用,似有取代临床医生之趋势。然而,人工智能大语言模型存在专业性差、缺乏个性化理解、无法正确处理伦理道德问题等缺点。期待通过不断的技术改进,进一步提升ChatGPT在医学领域的精准度和可信度,以更好地辅助医疗专业人员提供优质的医疗服务。亦应谨慎对待其局限性,始终保持医生的主导地位和责任。

基于态靶辨治代谢疾病智能辅助诊疗系统的研究与设计

目的:基于“态靶辨证”中医诊疗思维,利用人工智能技术为代谢病中医辅助诊疗提供智能化支持。方法:通过自然语言处理技术,对医学文献和专家经验进行信息抽取和实体识别,构建包含疾病状态、治疗靶点及相关辨证规则的术语知识库。在此基础上,利用图数据库和深度学习算法,构建态靶辨证知识图谱,表达不同疾病状态与治疗靶点之间的复杂关系。结果:通过模型训练和知识推理,结合病例的具体症状和体质,推荐相应的中医治疗方案,提高了代谢病辅助诊疗的准确性和效率。结论:本研究为中医辨证施治提供了新的方法和思路,不断迭代优化算法,拓展临床应用,推动中医药辅助诊疗的发展和完善,为中医药传承创新奠定良好的技术基础。

人工智能在心血管外科方面的应用研究进展

随着ChatGPT引发的人工智能(Artificial Intelligence,AI)热潮,AI在辅助心血管外科疾病诊疗中成为医学领域研究的热点之一。基于AI和深度学习技术开发的一系列辅助系统,包括疾病预测、风险分级、诊断和治疗方案规划等,为提高临床诊疗效率和准确性、降低错漏失误率、减少医疗资源浪费以及解决医疗资源短缺问题提供了新的途径和方法。本文首先总结分析了心血管外科与AI关注的共同点,随后列举了AI在血管外科手术和心外科手术中的应用实例,并深入分析其异同以及各种应用之间的系统关系,最后探讨了AI技术在心血管外科疾病领域的指导意义和研究进展,旨在为新一代AI辅助诊断和治疗方案规划软件的开发以及在心血管外科临床实践中的落地转化和推广应用提供参考。

基于深度学习神经网络技术的脊柱椎弓根螺钉自动规划研究

目的:针对骨科手术机器人螺钉手工规划效率低下的问题,实现基于CT的脊柱椎弓根螺钉自动、高效、高质量规划。方法:采用深度学习神经网络对标注分割和螺钉的CT图像进行有监督的机器学习,实现脊柱椎弓根螺钉的自动规划;本实验使用44例腰椎CT共440枚螺钉作为训练集,使用11例CT生成110枚螺钉作为测试集,以手工规划作为对照组,通过盲法专家评价评估螺钉规划效果,并通过记录规划时间评估规划效率。结果:该自动规划方法生成的螺钉规划临床可用率为95.4%,自动规划时间与平均手工规划时间分别为68.8 s和177.6 s。结论:该自动规划方法可初步实现高效、高质量的脊柱椎弓根螺钉自动规划,但仍需临床医生监督复核。

中医住培基地住培医生纯中医诊疗能力培养的问题与对策

全国268家中医住院医师规范化培训(以下简称“住培”)基地、协同单位和基层培养基地是培养中医临床医生的摇篮。中医住培自开展以来,取得了丰硕的成果,但同时也暴露出一些问题,尤其是中医住培基地在培养住培医生纯中医诊疗能力方面面临一些问题与挑战,需要制定相应的对策予以解决,以全面提升住培医生的纯中医诊疗能力。

人工智能在胃癌诊治中的研究趋势:20年的文献计量学分析

背景近年来有关将人工智能(AI)应用于胃癌诊治的研究日益增多,但目前尚未有研究者使用文献计量学分析法对其进行系统的分析。目的对将AI应用于胃癌诊治的相关研究进行分析,探讨2003-2022年研究的热点及发展趋势。方法于2022-11-06,计算机检索Web of Science(WOS)核心合集数据库,获取将AI应用于胃癌诊治的相关研究。通过文献计量学分析法,采用VOSviewer 1.6.18软件对国家(地区)间、机构间、作者间的合作情况、共被引作者情况、关键词共现与叠加情况进行可视化分析,采用CiteSpace 5.7.R5软件进行机构中介中心性分析、期刊双图叠加分析、近6年共被引文献聚类分析、共被引文献聚类时间线图分析及参考文献突现分析。采用Excel 2019软件绘制发文量条形图,以及国家(地区)、机构、期刊、作者、共被引作者、引用参考文献和关键词的描述性分析表格。结果共纳入703篇文献,2003-2022年将AI应用于胃癌诊治的相关研究年发文量整体呈上升趋势,2017年后年发文量快速增长,其中2019-2021年增长最为迅速。发文量最多的国家(地区)、机构和作者分别为中国、中国科学院和TADA TOMOHIRO。共被引次数排在前3的作者BRAY FREDDIE、HIRASAWA TOSHIAKI和JIANG YUMING对该领域做出了重要贡献。Frontiers in Oncology是发文量最多的期刊,Gastrointestinal Endoscopy是刊载将AI应用于胃癌诊治相关研究、发文量排在前10的期刊中最具影响力的期刊。施引期刊的所属领域主要集中在“Medicine,Medical,Clinical”(“药物,医学,临床”)、“Molecular,Biology,Immunology”(“分子,生物学,免疫学”)2个领域;被引期刊的所属领域主要集中在“Molecular,Biology,Genetics”(“分子,生物学,遗传学”)、“Health,Nursing,Medicine”(“健康,护理,药物”)2个领域。共被引次数排在首位的参考文献为Global cancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries。根据关键词聚类结果可将所有关键词分为4类:AI辅助胃癌的生物学研究、AI辅助胃癌的内镜诊断、AI辅助胃癌的病理诊断和AI辅助胃癌的非内镜治疗及预后预测。深度学习、卷积神经网络、影像组学、消化道内镜、病理学和免疫治疗是目前的研究热点。结论AI在胃癌诊治中具有广阔的应用前景,越来越多的学者致力于开展胃癌诊治的AI研究。目前,AI在胃癌的生物学、诊断、分期、疗效评估和预后预测方面得到了广泛的研究。本研究结果可为从事AI与胃癌相关研究工作的学者提供参考。

人工智能技术在当前生物学获益为主的胰腺癌诊疗模式中的发展方向

胰腺癌恶性程度高,发病隐匿,早期转移,预后极差。如何提高早期诊断率、改善病人预后是目前研究焦点。当前传统手术治疗虽可能一定程度上改善部分病人预后,但远期疗效仍较差。生物学获益是当前胰腺癌治疗的新模式,此模式下做好全过程监测、精准制定治疗方案及预测疗效至关重要。人工智能技术因其强大的数据处理、图像识别功能,较高的学习效率,在多学科专家通力合作下可建立相对完美的预测模型,协助临床医师制定优化方案,采用精准医疗模式及个体化治疗方案,使胰腺癌病人获益。

智慧放疗系统的构建与应用

目的:利用信息化手段实现放疗流程的高效管理。方法:基于标准化术语集构建放疗知识库,设计智慧放疗系统。基于对知识库的检索调用,实现治疗过程中全周期辅助核查和智能提醒,并根据检索引擎对病历文书进行规则质控,构建患者评估体系,根据诊疗数据自动评估患者状态,同时支持根据评估结果辅助诊疗,对疗程进行修正并解释修正原因。结果:放疗知识库累计产生5万余条标准化术语,30余万条放疗数据,每份病历录入时间平均节省15分钟,对病历内容中用药、检查检验、放疗剂量等30类节点共生成9万余次智能提醒,降低了60余类与放疗相关高风险场景下的医疗风险;根据患者体征和病情变化,对1万多名患者的疗程进行修正优化。结论:智慧放疗系统减轻了医生负担,优化了放疗流程,提高了病历质量和医疗质量。

人工智能辅助中医辨证的关键问题与技术挑战

中医辨证论治是传统医学体系的核心,几千年来为中华民族的健康发展发挥着不可或缺的作用。近年来,在多部门的协同推进下,我国中医科技创新能力持续提升。中医辨证与人工智能、大数据等多学科交叉融合,取得新的进展,工程化前沿方法和技术为突破中医辨证理论瓶颈提供了有效路径。本文以新时代中医诊断现代化、智能化发展为背景,总结中医辨证的基本原理,概括人工智能辅助中医辨证的基本过程以及多模态数据融合、症状关联性分析、证候量化、证候推理、中医药大模型等关键问题,总结了每个环节的研究思路和发展现状,进而根据现有不足分析当前发展面临的可利用公开数据较少且质量参差不齐,辨证模型单一、普适性不佳,辨证模型的可解释性不足且存在差异,辨证模型结果评价存在局限、缺乏可信度等挑战。研究建议,加强数据整合与质量把控,深度融合人工智能技术与中医辨证思维、加强模型可解释性,发展中医药细分领域的大语言模型,加强智能中医人才队伍建设、鼓励多领域专家合作,完善国际标准和法律法规、加强国际合作与交流等,以期为智能中医诊疗的技术探索和科技创新提供参考。